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Wang Y, Zeng D, Wang Y, Tong X. One-Step Regularized Estimator for High-Dimensional Regression Models. Stat Sin 2025. [DOI: 10.5705/ss.202022.0065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
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Lin DY, Xu Y, Gu Y, Sunny SK, Moore Z, Zeng D. Impact of Booster Vaccination Interval on SARS-CoV-2 Infection, Hospitalization, and Death. Int J Infect Dis 2024:107084. [PMID: 38705567 DOI: 10.1016/j.ijid.2024.107084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2024] [Revised: 04/17/2024] [Accepted: 05/01/2024] [Indexed: 05/07/2024] Open
Abstract
OBJECTIVES We investigated how booster interval affects the risks of SARS-CoV-2 infection and Covid-19-related hospitalization and death in different age groups. METHODS We collected data on booster receipts and Covid-19 outcomes between September 22, 2021 and February 9, 2023 for 5,769,205 North Carolina residents ≥12 years of age who had completed their primary vaccination series. We related Covid-19 outcomes to baseline characteristics and booster doses through Cox regression models. RESULTS For adults ≥65 years of age, boosting every 9 months was associated with proportionate reductions (compared with no boosting) of 18.9% (95% CI, 18.5 to 19.4) in the cumulative frequency of infection, 37.8% (95% CI, 35.3 to 40.3) in the cumulative risk of hospitalization or death, and 40.9% (95% CI, 37.2 to 44.7) in the cumulative risk of death at two years after completion of primary vaccination. The reductions were lower by boosting every 12 months and higher by boosting every 6 months. The reductions were smaller for individuals 12-64 years of age. CONCLUSION Boosting at a shorter interval was associated with a greater reduction in Covid-19 outcomes, especially hospitalization and death. Frequent boosting conferred greater benefits for individuals aged ≥65 than for individuals aged 12-64.
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Affiliation(s)
- Dan-Yu Lin
- University of North Carolina at Chapel Hill Gillings School of Global Public Health.
| | - Yangjianchen Xu
- University of North Carolina at Chapel Hill Gillings School of Global Public Health
| | - Yu Gu
- University of North Carolina at Chapel Hill Gillings School of Global Public Health
| | - Shadia K Sunny
- CDC Foundation at North Carolina Department of Health and Human Services
| | - Zack Moore
- North Carolina Department of Health and Human Services
| | - Donglin Zeng
- University of North Carolina at Chapel Hill Gillings School of Global Public Health
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Stickel AM, Mendoza A, Tarraf W, Kuwayama S, Kaur S, Morlett Paredes A, Daviglus ML, Testai FD, Zeng D, Isasi CR, Baiduc RR, Dinces E, Lee DJ, González HM. Hearing Loss and Associated 7-Year Cognitive Outcomes Among Hispanic and Latino Adults. JAMA Otolaryngol Head Neck Surg 2024; 150:385-392. [PMID: 38512278 PMCID: PMC10958383 DOI: 10.1001/jamaoto.2024.0184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 01/29/2024] [Indexed: 03/22/2024]
Abstract
Importance Hearing loss appears to have adverse effects on cognition and increases risk for cognitive impairment. These associations have not been thoroughly investigated in the Hispanic and Latino population, which faces hearing health disparities. Objective To examine associations between hearing loss with 7-year cognitive change and mild cognitive impairment (MCI) prevalence among a diverse cohort of Hispanic/Latino adults. Design, Setting, and Participants This cohort study used data from a large community health survey of Hispanic Latino adults in 4 major US cities. Eligible participants were aged 50 years or older at their second visit to study field centers. Cognitive data were collected at visit 1 and visit 2, an average of 7 years later. Data were last analyzed between September 2023 and January 2024. Exposure Hearing loss at visit 1 was defined as a pure-tone average (500, 1000, 2000, and 4000 Hz) greater than 25 dB hearing loss in the better ear. Main outcomes and measures Cognitive data were collected at visit 1 and visit 2, an average of 7 years later and included measures of episodic learning and memory (the Brief-Spanish English Verbal Learning Test Sum of Trials and Delayed Recall), verbal fluency (word fluency-phonemic fluency), executive functioning (Trails Making Test-Trail B), and processing speed (Digit-Symbol Substitution, Trails Making Test-Trail A). MCI at visit 2 was defined using the National Institute on Aging-Alzheimer Association criteria. Results A total of 6113 Hispanic Latino adults were included (mean [SD] age, 56.4 [8.1] years; 3919 women [64.1%]). Hearing loss at visit 1 was associated with worse cognitive performance at 7-year follow-up (global cognition: β = -0.11 [95% CI, -0.18 to -0.05]), equivalent to 4.6 years of aging and greater adverse change (slowing) in processing speed (β = -0.12 [95% CI, -0.23 to -0.003]) equivalent to 5.4 years of cognitive change due to aging. There were no associations with MCI. Conclusions and relevance The findings of this cohort study suggest that hearing loss decreases cognitive performance and increases rate of adverse change in processing speed. These findings underscore the need to prevent, assess, and treat hearing loss in the Hispanic and Latino community.
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Affiliation(s)
- Ariana M. Stickel
- Department of Psychology, San Diego State University, San Diego, California
| | - Alonzo Mendoza
- Department of Neurosciences, University of California, San Diego, La Jolla
| | - Wassim Tarraf
- Institute of Gerontology & Department of Healthcare Sciences, Wayne State University, Detroit, Michigan
| | - Sayaka Kuwayama
- Department of Neurosciences, University of California, San Diego, La Jolla
| | - Sonya Kaur
- Department of Neurology, University of Miami Miller School of Medicine, Miami, Florida
| | | | - Martha L. Daviglus
- Institute for Minority Health Research, College of Medicine, University of Illinois at Chicago, Chicago
| | - Fernando D. Testai
- Department of Neurology & Rehabilitation, College of Medicine, University of Illinois at Chicago, Chicago
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina, Chapel Hill
| | - Carmen R. Isasi
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, New York
| | - Rachael R. Baiduc
- Speech, Language, and Hearing Sciences, University of Colorado Boulder, Boulder
| | - Elizabeth Dinces
- Department of Otorhinolaryngology, Albert Einstein College of Medicine, Bronx, New York
| | - David J. Lee
- Department of Epidemiology & Public Health, University of Miami, Miami, Florida
| | - Hector M. González
- Department of Neurosciences, University of California, San Diego, La Jolla
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González HM, Tarraf W, Stickel AM, Morlett A, González KA, Ramos AR, Rundek T, Gallo LC, Talavera GA, Daviglus ML, Lipton RB, Isasi C, Lamar M, Zeng D, DeCarli C. Glycemic Control, Cognitive Aging, and Impairment Among Diverse Hispanics/Latinos: Study of Latinos-Investigation of Neurocognitive Aging (Hispanic Community Health Study/Study of Latinos). Diabetes Care 2024:dc232003. [PMID: 38684486 DOI: 10.2337/dc23-2003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 04/03/2024] [Indexed: 05/02/2024]
Abstract
OBJECTIVE Hispanics/Latinos in the United States have the highest prevalence of undiagnosed and untreated diabetes and are at increased risk for cognitive impairment. In this study, we examine glycemic control in relation to cognitive aging and impairment in a large prospective cohort of middle-aged and older Hispanics/Latinos of diverse heritages. RESEARCH DESIGN AND METHODS Study of Latinos-Investigation of Neurocognitive Aging (SOL-INCA) is a Hispanic Community Health Study/Study of Latinos (HCHS/SOL) ancillary study. HCHS/SOL is a multisite (Bronx, NY; Chicago, IL; Miami, FL; and San Diego, CA), probability sampled prospective cohort study. SOL-INCA enrolled 6,377 diverse Hispanics/Latinos age 50 years and older (2016-2018). The primary outcomes were cognitive function, 7-year cognitive decline and mild cognitive impairment (MCI). The primary glycemia exposure variables were measured from fasting blood samples collected at HCHS/SOL visit 1 (2008-2011). RESULTS Visit 1 mean age was 56.5 years ± 8.2 SD, and the average glycosylated hemoglobin A1C (HbA1c) was 6.12% (43.5 ± 14.6 mmol/mol). After covariates adjustment, higher HbA1c was associated with accelerated 7-year global (b = -0.045; 95% CI = -0.070; -0.021; in z-score units) and executive cognitive decline, and a higher prevalence of MCI (odds ratio = 1.20; 95% CI = 1.11;1.29). CONCLUSIONS Elevated HbA1c levels were associated with 7-year executive cognitive decline and increased MCI risk among diverse middle-aged and older Hispanics/Latinos. Our findings indicate that poor glycemic control in midlife may pose significant risks for cognitive decline and MCI later in life among Hispanics/Latinos of diverse heritages.
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Affiliation(s)
- Hector M González
- Department of Neurosciences and the Shiley-Marcos Alzheimer's Disease Research Center, University of California San Diego, San Diego, California
| | - Wassim Tarraf
- Institute of Gerontology & Department of Healthcare Sciences, Wayne State University, Detroit, Michigan
| | - Ariana M Stickel
- Department of Psychology, San Diego State University, San Diego, California
| | - Alejandra Morlett
- Department of Neurosciences and the Shiley-Marcos Alzheimer's Disease Research Center, University of California San Diego, San Diego, California
| | - Kevin A González
- Department of Neurosciences and the Shiley-Marcos Alzheimer's Disease Research Center, University of California San Diego, San Diego, California
| | - Alberto R Ramos
- Department of Neurology and Evelyn F. McKnight Brain Institute, University of Miami, Miami, Florida
| | - Tatjana Rundek
- Department of Neurology and Evelyn F. McKnight Brain Institute, University of Miami, Miami, Florida
| | - Linda C Gallo
- Department of Psychology, San Diego State University, San Diego, California
| | - Gregory A Talavera
- Department of Psychology, San Diego State University, San Diego, California
| | - Martha L Daviglus
- Institute for Minority Health Research, University of Illinois College of Medicine at Chicago, Chicago, Illinois
| | | | - Carmen Isasi
- Albert Einstein College of Medicine, New York, NY
| | - Melissa Lamar
- Institute for Minority Health Research, University of Illinois College of Medicine at Chicago, Chicago, Illinois
- Department of Psychiatry & Behavioral Sciences and Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois
| | - Donglin Zeng
- Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina
| | - Charles DeCarli
- Department of Neurology and Alzheimer's Disease Center, University of California Davis, Sacramento, California
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Lin DY, Wang J, Gu Y, Zeng D. Evaluating treatment efficacy in hospitalized COVID-19 patients, with applications to Adaptive COVID-19 Treatment Trials. Clin Trials 2024:17407745241238443. [PMID: 38618926 DOI: 10.1177/17407745241238443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
BACKGROUND The current endpoints for therapeutic trials of hospitalized COVID-19 patients capture only part of the clinical course of a patient and have limited statistical power and robustness. METHODS We specify proportional odds models for repeated measures of clinical status, with a common odds ratio of lower severity over time. We also specify the proportional hazards model for time to each level of improvement or deterioration of clinical status, with a common hazard ratio for overall treatment benefit. We apply these methods to Adaptive COVID-19 Treatment Trials. RESULTS For remdesivir versus placebo, the common odds ratio was 1.48 (95% confidence interval (CI) = 1.23-1.79; p < 0.001), and the common hazard ratio was 1.27 (95% CI = 1.09-1.47; p = 0.002). For baricitinib plus remdesivir versus remdesivir alone, the common odds ratio was 1.32 (95% CI = 1.10-1.57; p = 0.002), and the common hazard ratio was 1.30 (95% CI = 1.13-1.49; p < 0.001). For interferon beta-1a plus remdesivir versus remdesivir alone, the common odds ratio was 0.95 (95% CI = 0.79-1.14; p = 0.56), and the common hazard ratio was 0.98 (95% CI = 0.85-1.12; p = 0.74). CONCLUSIONS The proposed methods comprehensively characterize the treatment effects on the entire clinical course of a hospitalized COVID-19 patient.
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Affiliation(s)
- Dan-Yu Lin
- Department of Biostatistics, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jianqiao Wang
- Department of Biostatistics, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yu Gu
- Department of Biostatistics, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Donglin Zeng
- Department of Biostatistics, The University of Michigan, Ann Arbor, MI, USA
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Graves LV, Tarraf W, Gonzalez K, Bondi MW, Gallo LC, Isasi CR, Daviglus M, Lamar M, Zeng D, Cai J, González HM. Characterizing cognitive profiles in diverse middle-aged and older Hispanics/Latinos: Study of Latinos-Investigation of Neurocognitive Aging (HCHS/SOL). Alzheimers Dement (Amst) 2024; 16:e12592. [PMID: 38655549 PMCID: PMC11035970 DOI: 10.1002/dad2.12592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 03/26/2024] [Accepted: 03/27/2024] [Indexed: 04/26/2024]
Abstract
Introduction We investigated cognitive profiles among diverse, middle-aged and older Hispanic/Latino adults in the Study of Latinos-Investigation of Neurocognitive Aging (SOL-INCA) cohort using a cross-sectional observational study design. Methods Based on weighted descriptive statistics, the average baseline age of the target population was 56.4 years, slightly more than half were women (54.6%), and 38.4% reported less than a high school education. We used latent profile analysis of demographically adjusted z scores on SOL-INCA neurocognitive tests spanning domains of verbal memory, language, processing speed, and executive function. Results Statistical fit assessment indices combined with clinical interpretation suggested five profiles: (1) a Higher Global group performing in the average-to-high-average range across all cognitive and instrumental activity of daily living (IADL) tests (13.8%); (2) a Higher Memory group with relatively high performance on memory tests but average performance across all other cognitive/IADL tests (24.6%); (3) a Lower Memory group with relatively low performance on memory tests but average performance across all other cognitive/IADL tests (32.8%); (4) a Lower Executive Function group with relatively low performance on executive function and processing speed tests but average-to-low-average performance across all other cognitive/IADL tests (16.6%); and (5) a Lower Global group performing low-average-to-mildly impaired across all cognitive/IADL tests (12.1%). Discussion Our results provide evidence of heterogeneity in the cognitive profiles of a representative, community-dwelling sample of diverse Hispanic/Latino adults. Our analyses yielded cognitive profiles that may assist efforts to better understand the early cognitive changes that may portend Alzheimer's disease and related dementias among diverse Hispanics/Latinos. Highlights The present study characterized cognitive profiles among diverse middle-aged and older Hispanic/Latino adults.Latent profile analysis of neurocognitive test scores was the primary analysis conducted.The target population consists of middle-aged and older Hispanic/Latino adults enrolled in the Hispanic Community Health Study/Study of Latinos and ancillary Study of Latinos - Investigation of Neurocognitive Aging.
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Affiliation(s)
- Lisa V. Graves
- Department of PsychologyCalifornia State University San MarcosSan MarcosCaliforniaUSA
| | - Wassim Tarraf
- Institute of Gerontology & Department of Healthcare SciencesWayne State UniversityDetroitMichiganUSA
| | - Kevin Gonzalez
- Department of NeurosciencesUniversity of California San DiegoLa JollaCaliforniaUSA
| | - Mark W. Bondi
- Department of PsychiatryUniversity of California San DiegoSchool of MedicineLa JollaCaliforniaUSA
- Veterans Affairs San Diego Healthcare SystemSan DiegoCaliforniaUSA
| | - Linda C. Gallo
- Department of PsychologySan Diego State UniversitySan DiegoCaliforniaUSA
| | - Carmen R. Isasi
- Department of Epidemiology & Population HealthAlbert Einstein College of MedicineJack and Pearl Resnick CampusBronxNew YorkUSA
| | - Martha Daviglus
- Institute for Minority Health ResearchUniversity of Illinois at ChicagoCollege of MedicineChicagoIllinoisUSA
| | - Melissa Lamar
- Institute for Minority Health ResearchUniversity of Illinois at ChicagoCollege of MedicineChicagoIllinoisUSA
- Rush Alzheimer's Disease CenterRush University Medical CenterChicagoIllinoisUSA
| | - Donglin Zeng
- Department of BiostatisticsUniversity of North CarolinaChapel HillNorth CarolinaUSA
| | - Jianwen Cai
- Department of BiostatisticsUniversity of North CarolinaChapel HillNorth CarolinaUSA
| | - Hector M. González
- Department of NeurosciencesUniversity of California San DiegoLa JollaCaliforniaUSA
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Tan X, Wang W, Zeng D, Liu GF, Diao G, Jafari N, Alt EM, Ibrahim JG. Safety signal detection with control of latent factors. Stat Med 2024; 43:1397-1418. [PMID: 38297431 DOI: 10.1002/sim.10015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2023] [Revised: 10/26/2023] [Accepted: 12/27/2023] [Indexed: 02/02/2024]
Abstract
Postmarket drug safety database like vaccine adverse event reporting system (VAERS) collect thousands of spontaneous reports annually, with each report recording occurrences of any adverse events (AEs) and use of vaccines. We hope to identify signal vaccine-AE pairs, for which certain vaccines are statistically associated with certain adverse events (AE), using such data. Thus, the outcomes of interest are multiple AEs, which are binary outcomes and could be correlated because they might share certain latent factors; and the primary covariates are vaccines. Appropriately accounting for the complex correlation among AEs could improve the sensitivity and specificity of identifying signal vaccine-AE pairs. We propose a two-step approach in which we first estimate the shared latent factors among AEs using a working multivariate logistic regression model, and then use univariate logistic regression model to examine the vaccine-AE associations after controlling for the latent factors. Our simulation studies show that this approach outperforms current approaches in terms of sensitivity and specificity. We apply our approach in analyzing VAERS data and report our findings.
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Affiliation(s)
- Xianming Tan
- Department of Biostatistics at Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - William Wang
- Merck and Co., Inc., North Wales, Pennsylvania, USA
| | - Donglin Zeng
- Department of Biostatistics at Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | | | - Guoqing Diao
- Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, George Washington University, Washington, DC, USA
| | | | - Ethan M Alt
- Department of Biostatistics at Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Joseph G Ibrahim
- Department of Biostatistics at Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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Xie S, Zeng D, Wang Y. Identifying temporal pathways using biomarkers in the presence of latent non-Gaussian components. Biometrics 2024; 80:ujae033. [PMID: 38708763 DOI: 10.1093/biomtc/ujae033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 03/21/2024] [Accepted: 04/09/2024] [Indexed: 05/07/2024]
Abstract
Time-series data collected from a network of random variables are useful for identifying temporal pathways among the network nodes. Observed measurements may contain multiple sources of signals and noises, including Gaussian signals of interest and non-Gaussian noises, including artifacts, structured noise, and other unobserved factors (eg, genetic risk factors, disease susceptibility). Existing methods, including vector autoregression (VAR) and dynamic causal modeling do not account for unobserved non-Gaussian components. Furthermore, existing methods cannot effectively distinguish contemporaneous relationships from temporal relations. In this work, we propose a novel method to identify latent temporal pathways using time-series biomarker data collected from multiple subjects. The model adjusts for the non-Gaussian components and separates the temporal network from the contemporaneous network. Specifically, an independent component analysis (ICA) is used to extract the unobserved non-Gaussian components, and residuals are used to estimate the contemporaneous and temporal networks among the node variables based on method of moments. The algorithm is fast and can easily scale up. We derive the identifiability and the asymptotic properties of the temporal and contemporaneous networks. We demonstrate superior performance of our method by extensive simulations and an application to a study of attention-deficit/hyperactivity disorder (ADHD), where we analyze the temporal relationships between brain regional biomarkers. We find that temporal network edges were across different brain regions, while most contemporaneous network edges were bilateral between the same regions and belong to a subset of the functional connectivity network.
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Affiliation(s)
- Shanghong Xie
- School of Statistics, Southwestern University of Finance and Economics, Chengdu 611130, China
| | - Donglin Zeng
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, United States
| | - Yuanjia Wang
- Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, NY 10032, United States
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Garrison-Desany HM, Meyers JL, Linnstaedt SD, House SL, Beaudoin FL, An X, Zeng D, Neylan TC, Clifford GD, Jovanovic T, Germine LT, Bollen KA, Rauch SL, Haran JP, Storrow AB, Lewandowski C, Musey PI, Hendry PL, Sheikh S, Jones CW, Punches BE, Swor RA, Gentile NT, Hudak LA, Pascual JL, Seamon MJ, Harris E, Pearson C, Peak DA, Domeier RM, Rathlev NK, O’Neil BJ, Sergot P, Sanchez LD, Bruce SE, Joormann J, Harte SE, McLean SA, Koenen KC, Denckla CA. Post-traumatic stress and future substance use outcomes: leveraging antecedent factors to stratify risk. Front Psychiatry 2024; 15:1249382. [PMID: 38525258 PMCID: PMC10957776 DOI: 10.3389/fpsyt.2024.1249382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 01/10/2024] [Indexed: 03/26/2024] Open
Abstract
Background Post-traumatic stress disorder (PTSD) and substance use (tobacco, alcohol, and cannabis) are highly comorbid. Many factors affect this relationship, including sociodemographic and psychosocial characteristics, other prior traumas, and physical health. However, few prior studies have investigated this prospectively, examining new substance use and the extent to which a wide range of factors may modify the relationship to PTSD. Methods The Advancing Understanding of RecOvery afteR traumA (AURORA) study is a prospective cohort of adults presenting at emergency departments (N = 2,943). Participants self-reported PTSD symptoms and the frequency and quantity of tobacco, alcohol, and cannabis use at six total timepoints. We assessed the associations of PTSD and future substance use, lagged by one timepoint, using the Poisson generalized estimating equations. We also stratified by incident and prevalent substance use and generated causal forests to identify the most important effect modifiers of this relationship out of 128 potential variables. Results At baseline, 37.3% (N = 1,099) of participants reported likely PTSD. PTSD was associated with tobacco frequency (incidence rate ratio (IRR): 1.003, 95% CI: 1.00, 1.01, p = 0.02) and quantity (IRR: 1.01, 95% CI: 1.001, 1.01, p = 0.01), and alcohol frequency (IRR: 1.002, 95% CI: 1.00, 1.004, p = 0.03) and quantity (IRR: 1.003, 95% CI: 1.001, 1.01, p = 0.001), but not with cannabis use. There were slight differences in incident compared to prevalent tobacco frequency and quantity of use; prevalent tobacco frequency and quantity were associated with PTSD symptoms, while incident tobacco frequency and quantity were not. Using causal forests, lifetime worst use of cigarettes, overall self-rated physical health, and prior childhood trauma were major moderators of the relationship between PTSD symptoms and the three substances investigated. Conclusion PTSD symptoms were highly associated with tobacco and alcohol use, while the association with prospective cannabis use is not clear. Findings suggest that understanding the different risk stratification that occurs can aid in tailoring interventions to populations at greatest risk to best mitigate the comorbidity between PTSD symptoms and future substance use outcomes. We demonstrate that this is particularly salient for tobacco use and, to some extent, alcohol use, while cannabis is less likely to be impacted by PTSD symptoms across the strata.
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Affiliation(s)
- Henri M. Garrison-Desany
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, United States
| | - Jacquelyn L. Meyers
- Department of Psychiatry and Behavioral Sciences, State University of New York Downstate Medical Center, New York City, NY, United States
| | - Sarah D. Linnstaedt
- Department of Anesthesiology, Institute for Trauma Recovery, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Stacey L. House
- Department of Emergency Medicine, Washington University School of Medicine, St. Louis, MO, United States
| | - Francesca L. Beaudoin
- Department of Epidemiology, Brown University, Providence, RI, United States
- Department of Emergency Medicine, Brown University, Providence, RI, United States
| | - Xinming An
- Department of Anesthesiology, Institute for Trauma Recovery, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Donglin Zeng
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, United States
| | - Thomas C. Neylan
- Departments of Psychiatry and Neurology, University of California San Francisco, San Francisco, CA, United States
| | - Gari D. Clifford
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, United States
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, United States
| | - Tanja Jovanovic
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, MI, United States
| | - Laura T. Germine
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, United States
- The Many Brains Project, Belmont, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
| | - Kenneth A. Bollen
- Department of Psychology and Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Scott L. Rauch
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, United States
- Department of Psychiatry, Harvard Medical School, Boston, MA, United States
- Department of Psychiatry, McLean Hospital, Belmont, MA, United States
| | - John P. Haran
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States
| | - Alan B. Storrow
- Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | | | - Paul I. Musey
- Department of Emergency Medicine, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Phyllis L. Hendry
- Department of Emergency Medicine, University of Florida College of Medicine -Jacksonville, Jacksonville, FL, United States
| | - Sophia Sheikh
- Department of Emergency Medicine, University of Florida College of Medicine -Jacksonville, Jacksonville, FL, United States
| | - Christopher W. Jones
- Department of Emergency Medicine, Cooper Medical School of Rowan University, Camden, NJ, United States
| | - Brittany E. Punches
- Department of Emergency Medicine, Ohio State University College of Medicine, Columbus, OH, United States
| | - Robert A. Swor
- Department of Emergency Medicine, Oakland University William Beaumont School of Medicine, Rochester, MI, United States
| | - Nina T. Gentile
- Department of Emergency Medicine, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, United States
| | - Lauren A. Hudak
- Department of Emergency Medicine, Emory University School of Medicine, Atlanta, GA, United States
| | - Jose L. Pascual
- Department of Surgery, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, United States
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Mark J. Seamon
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Department of Surgery, Division of Traumatology, Surgical Critical Care and Emergency Surgery, University of Pennsylvania, Philadelphia, PA, United States
| | - Erica Harris
- Department of Emergency Medicine, Einstein Medical Center, Philadelphia, PA, United States
| | - Claire Pearson
- Department of Emergency Medicine, Wayne State University, Ascension St. John Hospital, Detroit, MI, United States
| | - David A. Peak
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Robert M. Domeier
- Department of Emergency Medicine, Trinity Health-Ann Arbor, Ypsilanti, MI, United States
| | - Niels K. Rathlev
- Department of Emergency Medicine, University of Massachusetts Medical School-Baystate, Springfield, MA, United States
| | - Brian J. O’Neil
- Department of Emergency Medicine, Wayne State University, Detroit Receiving Hospital, Detroit, MI, United States
| | - Paulina Sergot
- Department of Emergency Medicine, McGovern Medical School at The University of Texas Health Science Center, Houston, TX, United States
| | - Leon D. Sanchez
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, United States
- Department of Emergency Medicine, Harvard Medical School, Boston, MA, United States
| | - Steven E. Bruce
- Department of Psychological Sciences, University of Missouri - St. Louis, St. Louis, MO, United States
| | - Jutta Joormann
- Department of Psychology, Yale University, New Haven, CT, United States
| | - Steven E. Harte
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, United States
- Department of Internal Medicine-Rheumatology, University of Michigan Medical School, Ann Arbor, MI, United States
| | - Samuel A. McLean
- Department of Emergency Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Psychiatry, Institute for Trauma Recovery, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Karestan C. Koenen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, United States
| | - Christy A. Denckla
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, United States
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10
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Stickel AM, Tarraf W, Kuwayama S, Wu B, Sundermann EE, Gallo LC, Lamar M, Daviglus M, Zeng D, Thyagarajan B, Isasi CR, Lipton RB, Cordero C, Perreira KM, Gonzalez HM, Banks SJ. Connections between reproductive health and cognitive aging among women enrolled in the HCHS/SOL and SOL-INCA. Alzheimers Dement 2024; 20:1944-1957. [PMID: 38160447 PMCID: PMC10947951 DOI: 10.1002/alz.13575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 11/01/2023] [Accepted: 11/13/2023] [Indexed: 01/03/2024]
Abstract
INTRODUCTION Reproductive health history may contribute to cognitive aging and risk for Alzheimer's disease, but this is understudied among Hispanic/Latina women. METHODS Participants included 2126 Hispanic/Latina postmenopausal women (44 to 75 years) from the Study of Latinos-Investigation of Neurocognitive Aging. Survey linear regressions separately modeled the associations between reproductive health measures (age at menarche, history of oral contraceptive use, number of pregnancies, number of live births, age at menopause, female hormone use at Visit 1, and reproductive span) with cognitive outcomes at Visit 2 (performance, 7-year change, and mild cognitive impairment [MCI] prevalence). RESULTS Younger age at menarche, oral contraceptive use, lower pregnancies, lower live births, and older age at menopause were associated with better cognitive performance. Older age at menarche was protective against cognitive change. Hormone use was linked to lower MCI prevalence. DISCUSSION Several aspects of reproductive health appear to impact cognitive aging among Hispanic/Latina women.
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Affiliation(s)
- Ariana M. Stickel
- Department of PsychologySan Diego State UniversitySan DiegoCaliforniaUSA
| | - Wassim Tarraf
- Institute of Gerontology & Department of Healthcare SciencesWayne State UniversityDetroitMichiganUSA
| | - Sayaka Kuwayama
- Department of NeurosciencesUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Benson Wu
- Department of NeurosciencesUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Erin E. Sundermann
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Linda C. Gallo
- Department of PsychologySan Diego State UniversitySan DiegoCaliforniaUSA
| | - Melissa Lamar
- Institute for Minority Health ResearchUniversity of Illinois at ChicagoCollege of MedicineChicagoIllinoisUSA
- Rush Alzheimer's Disease Research Center and the Department of Psychiatry and Behavioral SciencesRush University Medical CenterChicagoIllinoisUSA
| | - Martha Daviglus
- Institute for Minority Health ResearchUniversity of Illinois at ChicagoCollege of MedicineChicagoIllinoisUSA
| | - Donglin Zeng
- Department of BiostatisticsUniversity of North CarolinaChapel HillNorth CarolinaUSA
| | - Bharat Thyagarajan
- Department of Laboratory Medicine and PathologyUniversity of Minnesota Medical SchoolMinneapolisMinnesotaUSA
| | - Carmen R. Isasi
- Department of Epidemiology & Population HealthAlbert Einstein College of MedicineBronxNew YorkUSA
| | - Richard B. Lipton
- Department of Epidemiology & Population HealthAlbert Einstein College of MedicineBronxNew YorkUSA
- Department of NeurologyAlbert Einstein College of MedicineBronxNew YorkUSA
| | | | - Krista M. Perreira
- Department of Social MedicineUniversity of North CarolinaChapel HillNorth CarolinaUSA
| | - Hector M. Gonzalez
- Department of NeurosciencesUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Sarah J. Banks
- Department of NeurosciencesUniversity of California, San DiegoLa JollaCaliforniaUSA
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11
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Wang G, Yang C, Zeng D, Wang J, Mao H, Xu Y, Jiang C, Wang Z. Case report: Successful treatment of a rare HER2-positive advanced breast squamous cell carcinoma. Front Pharmacol 2024; 15:1332574. [PMID: 38455963 PMCID: PMC10917954 DOI: 10.3389/fphar.2024.1332574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 02/05/2024] [Indexed: 03/09/2024] Open
Abstract
Background: Breast squamous cell carcinoma (SCC) is an uncommon and highly aggressive variant of metaplastic breast cancer. Despite its rarity, there is currently no consensus on treatment guidelines for this specific subtype. Previous studies have demonstrated that chemotherapy alone has limited efficacy in treating breast SCC. However, the potential for targeted therapy in combination with chemotherapy holds promise for future treatment options. Case presentation: In this case report, we present a patient with advanced HER2-positive breast SCC, exhibiting a prominent breast mass, localized ulcers, and metastases in the lungs and brain. Our treatment approach involved the administration of HER2-targeted drugs in conjunction with paclitaxel, resulting in a sustained control of tumor growth. Conclusion: This case represents a rare occurrence of HER2-positive breast SCC, with limited available data on the efficacy of previous HER2-targeted drugs in treating such patients. Our study presents the first application of HER2-targeted drugs in this particular case, offering novel therapeutic insights for future considerations. Additionally, it is imperative to conduct further investigations to assess the feasibility of treatment options in a larger cohort of patients.
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Affiliation(s)
- Gui Wang
- Department of General Surgery, Longquan People’s Hospital, Lishui, China
- Department of Breast Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chenghui Yang
- Department of Breast Surgery, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Donglin Zeng
- Department of General Surgery, Longquan People’s Hospital, Lishui, China
| | - Jihao Wang
- Department of General Surgery, Longquan People’s Hospital, Lishui, China
| | - Huaxin Mao
- Department of General Surgery, Longquan People’s Hospital, Lishui, China
| | - Yu Xu
- Department of General Surgery, Longquan People’s Hospital, Lishui, China
| | - Chao Jiang
- Academy of Chinese Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China
| | - Zhen Wang
- Department of Breast Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Tumor Microenvironment and Immune Therapy of Zhejiang Province, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Cancer Center, Zhejiang University, Hangzhou, China
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12
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Wang J, Zeng D, Lin DY. Fitting the Cox proportional hazards model to big data. Biometrics 2024; 80:ujae018. [PMID: 38497824 PMCID: PMC10946235 DOI: 10.1093/biomtc/ujae018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 01/14/2024] [Accepted: 02/20/2024] [Indexed: 03/19/2024]
Abstract
The semiparametric Cox proportional hazards model, together with the partial likelihood principle, has been widely used to study the effects of potentially time-dependent covariates on a possibly censored event time. We propose a computationally efficient method for fitting the Cox model to big data involving millions of study subjects. Specifically, we perform maximum partial likelihood estimation on a small subset of the whole data and improve the initial estimator by incorporating the remaining data through one-step estimation with estimated efficient score functions. We show that the final estimator has the same asymptotic distribution as the conventional maximum partial likelihood estimator using the whole dataset but requires only a small fraction of computation time. We demonstrate the usefulness of the proposed method through extensive simulation studies and an application to the UK Biobank data.
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Affiliation(s)
- Jianqiao Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Dan-Yu Lin
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
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13
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Xu Y, Zeng D, Lin DY. Proportional rates models for multivariate panel count data. Biometrics 2024; 80:ujad011. [PMID: 38364799 PMCID: PMC10871866 DOI: 10.1093/biomtc/ujad011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 08/22/2023] [Accepted: 11/13/2023] [Indexed: 02/18/2024]
Abstract
Multivariate panel count data arise when there are multiple types of recurrent events, and the observation for each study subject consists of the number of recurrent events of each type between two successive examinations. We formulate the effects of potentially time-dependent covariates on multiple types of recurrent events through proportional rates models, while leaving the dependence structures of the related recurrent events completely unspecified. We employ nonparametric maximum pseudo-likelihood estimation under the working assumptions that all types of events are independent and each type of event is a nonhomogeneous Poisson process, and we develop a simple and stable EM-type algorithm. We show that the resulting estimators of the regression parameters are consistent and asymptotically normal, with a covariance matrix that can be estimated consistently by a sandwich estimator. In addition, we develop a class of graphical and numerical methods for checking the adequacy of the fitted model. Finally, we evaluate the performance of the proposed methods through simulation studies and analysis of a skin cancer clinical trial.
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Affiliation(s)
- Yangjianchen Xu
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, United States
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, United States
| | - Dan-Yu Lin
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, United States
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14
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Hinojosa CA, Liew A, An X, Stevens JS, Basu A, van Rooij SJH, House SL, Beaudoin FL, Zeng D, Neylan TC, Clifford GD, Jovanovic T, Linnstaedt SD, Germine LT, Rauch SL, Haran JP, Storrow AB, Lewandowski C, Musey PI, Hendry PL, Sheikh S, Jones CW, Punches BE, Kurz MC, Swor RA, Hudak LA, Pascual JL, Seamon MJ, Datner EM, Chang AM, Pearson C, Peak DA, Merchant RC, Domeier RM, Rathlev NK, Sergot P, Sanchez LD, Bruce SE, Miller MW, Pietrzak RH, Joormann J, Pizzagalli DA, Sheridan JF, Harte SE, Elliott JM, Kessler RC, Koenen KC, McLean SA, Ressler KJ, Fani N. Associations of alcohol and cannabis use with change in posttraumatic stress disorder and depression symptoms over time in recently trauma-exposed individuals. Psychol Med 2024; 54:338-349. [PMID: 37309917 PMCID: PMC10716364 DOI: 10.1017/s0033291723001642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
BACKGROUND Several hypotheses may explain the association between substance use, posttraumatic stress disorder (PTSD), and depression. However, few studies have utilized a large multisite dataset to understand this complex relationship. Our study assessed the relationship between alcohol and cannabis use trajectories and PTSD and depression symptoms across 3 months in recently trauma-exposed civilians. METHODS In total, 1618 (1037 female) participants provided self-report data on past 30-day alcohol and cannabis use and PTSD and depression symptoms during their emergency department (baseline) visit. We reassessed participant's substance use and clinical symptoms 2, 8, and 12 weeks posttrauma. Latent class mixture modeling determined alcohol and cannabis use trajectories in the sample. Changes in PTSD and depression symptoms were assessed across alcohol and cannabis use trajectories via a mixed-model repeated-measures analysis of variance. RESULTS Three trajectory classes (low, high, increasing use) provided the best model fit for alcohol and cannabis use. The low alcohol use class exhibited lower PTSD symptoms at baseline than the high use class; the low cannabis use class exhibited lower PTSD and depression symptoms at baseline than the high and increasing use classes; these symptoms greatly increased at week 8 and declined at week 12. Participants who already use alcohol and cannabis exhibited greater PTSD and depression symptoms at baseline that increased at week 8 with a decrease in symptoms at week 12. CONCLUSIONS Our findings suggest that alcohol and cannabis use trajectories are associated with the intensity of posttrauma psychopathology. These findings could potentially inform the timing of therapeutic strategies.
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Affiliation(s)
- Cecilia A. Hinojosa
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Amanda Liew
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Xinming An
- Institute for Trauma Recovery, Department of Anesthesiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jennifer S. Stevens
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Archana Basu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Sanne J H. van Rooij
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Stacey L. House
- Department of Emergency Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Francesca L. Beaudoin
- Department of Emergency Medicine & Department of Health Services, Policy, and Practice, The Alpert Medical School of Brown University, Rhode Island Hospital and The Miriam Hospital, Providence, RI, USA
| | - Donglin Zeng
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Thomas C. Neylan
- Departments of Psychiatry and Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Gari D. Clifford
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Tanja Jovanovic
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, MI, USA
| | - Sarah D. Linnstaedt
- Institute for Trauma Recovery, Department of Anesthesiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Laura T. Germine
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA
- The Many Brains Project, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Scott L. Rauch
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, McLean Hospital, Belmont, MA, USA
| | - John P. Haran
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Alan B. Storrow
- Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Paul I. Musey
- Department of Emergency Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Phyllis L. Hendry
- Department of Emergency Medicine, University of Florida College of Medicine -Jacksonville, Jacksonville, FL, USA
| | - Sophia Sheikh
- Department of Emergency Medicine, University of Florida College of Medicine -Jacksonville, Jacksonville, FL, USA
| | - Christopher W. Jones
- Department of Emergency Medicine, Cooper Medical School of Rowan University, Camden, NJ, USA
| | - Brittany E. Punches
- Department of Emergency Medicine, Ohio State University College of Medicine, Columbus, OH, USA
- Ohio State University College of Nursing, Columbus, OH, USA
| | - Michael C. Kurz
- Department of Emergency Medicine, University of Alabama School of Medicine, Birmingham, AL, USA
- Department of Surgery, Division of Acute Care Surgery, University of Alabama School of Medicine, Birmingham, AL, USA
- Center for Injury Science, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Robert A. Swor
- Department of Emergency Medicine, Oakland University William Beaumont School of Medicine, Rochester, MI, USA
| | - Lauren A. Hudak
- Department of Emergency Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Jose L. Pascual
- Department of Surgery, Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mark J. Seamon
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Surgery, Division of Traumatology, Surgical Critical Care and Emergency Surgery, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth M. Datner
- Department of Emergency Medicine, Einstein Healthcare Network, Philadelphia, PA, USA
- Department of Emergency Medicine, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Anna M. Chang
- Department of Emergency Medicine, Jefferson University Hospitals, Philadelphia, PA, USA
| | - Claire Pearson
- Department of Emergency Medicine, Wayne State University, Ascension St. John Hospital, Detroit, MI, USA
| | - David A. Peak
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Roland C. Merchant
- Department of Emergency Medicine, Brigham and Women’s Hospital, Boston, MA, USA
| | - Robert M. Domeier
- Department of Emergency Medicine, Saint Joseph Mercy Hospital, Ypsilanti, MI, USA
| | - Niels K. Rathlev
- Department of Emergency Medicine, University of Massachusetts Medical School-Baystate, Springfield, MA, USA
| | - Paulina Sergot
- Department of Emergency Medicine, McGovern Medical School at UTHealth, Houston, TX, USA
| | - Leon D. Sanchez
- Department of Emergency Medicine, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Emergency Medicine, Harvard Medical School, Boston, MA, USA
| | - Steven E. Bruce
- Department of Psychological Sciences, University of Missouri - St. Louis, St. Louis, MO, USA
| | - Mark W. Miller
- National Center for PTSD, Behavioral Science Division, VA Boston Healthcare System, Boston, MA, USA
- Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
| | - Robert H. Pietrzak
- National Center for PTSD, Clinical Neurosciences Division, VA Connecticut Healthcare System, West Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Jutta Joormann
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Diego A. Pizzagalli
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA
| | - John F. Sheridan
- Division of Biosciences, Ohio State University College of Dentistry, Columbus, OH, USA
- Institute for Behavioral Medicine Research, OSU Wexner Medical Center, Columbus, OH, USA
| | - Steven E. Harte
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, USA
- Department of Internal Medicine-Rheumatology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - James M. Elliott
- Kolling Institute, University of Sydney, St Leonards, New South Wales, Australia
- Faculty of Medicine and Health, University of Sydney, Northern Sydney Local Health District, New South Wales, Australia
- Physical Therapy & Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Ronald C. Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Karestan C. Koenen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Samuel A. McLean
- Department of Emergency Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Institute for Trauma Recovery, Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kerry J. Ressler
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA
| | - Negar Fani
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
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15
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Li C, Tian Y, Zeng D, Shepherd BE. Asymptotic Properties for Cumulative Probability Models for Continuous Outcomes. Mathematics (Basel) 2023; 11:4896. [PMID: 38374966 PMCID: PMC10875740 DOI: 10.3390/math11244896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
Regression models for continuous outcomes frequently require a transformation of the outcome, which is often specified a priori or estimated from a parametric family. Cumulative probability models (CPMs) nonparametrically estimate the transformation by treating the continuous outcome as if it is ordered categorically. They thus represent a flexible analysis approach for continuous outcomes. However, it is difficult to establish asymptotic properties for CPMs due to the potentially unbounded range of the transformation. Here we show asymptotic properties for CPMs when applied to slightly modified data where bounds, one lower and one upper, are chosen and the outcomes outside the bounds are set as two ordinal categories. We prove the uniform consistency of the estimated regression coefficients and of the estimated transformation function between the bounds. We also describe their joint asymptotic distribution, and show that the estimated regression coefficients attain the semiparametric efficiency bound. We show with simulations that results from this approach and those from using the CPM on the original data are very similar when a small fraction of the data are modified. We reanalyze a dataset of HIV-positive patients with CPMs to illustrate and compare the approaches.
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Affiliation(s)
- Chun Li
- Division of Biostatistics, Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA 90033, USA
| | - Yuqi Tian
- Department of Biostatistics, Vanderbilt University, Nashville, TN 37203, USA
| | - Donglin Zeng
- Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Bryan E. Shepherd
- Department of Biostatistics, Vanderbilt University, Nashville, TN 37203, USA
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16
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Wang G, Liu Q, Chen G, Xia B, Zeng D, Chen G, Guo C. AI's deep dive into complex pediatric inguinal hernia issues: a challenge to traditional guidelines? Hernia 2023; 27:1587-1599. [PMID: 37843604 DOI: 10.1007/s10029-023-02900-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 09/19/2023] [Indexed: 10/17/2023]
Abstract
OBJECTIVE This study utilized ChatGPT, an artificial intelligence program based on large language models, to explore controversial issues in pediatric inguinal hernia surgery and compare its responses with the guidelines of the European Association of Pediatric Surgeons (EUPSA). METHODS Six contentious issues raised by EUPSA were submitted to ChatGPT 4.0 for analysis, for which two independent responses were generated for each issue. These generated answers were subsequently compared with systematic reviews and guidelines. To ensure content accuracy and reliability, a content analysis was conducted, and expert evaluations were solicited for validation. Content analysis evaluated the consistency or discrepancy between ChatGPT 4.0's responses and the guidelines. An expert scoring method assess the quality, reliability, and applicability of responses. The TF-IDF model tested the stability and consistency of the two responses. RESULTS The responses generated by ChatGPT 4.0 were mostly consistent with the guidelines. However, some differences and contradictions were noted. The average quality score was 3.33, reliability score was 2.75, and applicability score was 3.46 (out of 5). The average similarity between the two responses was 0.72 (out of 1), Content analysis and expert ratings yielded consistent conclusions, enhancing the credibility of our research. CONCLUSION ChatGPT can provide valuable responses to clinical questions, but it has limitations and requires further improvement. It is recommended to combine ChatGPT with other reliable data sources to improve clinical practice and decision-making.
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Affiliation(s)
- G Wang
- Department of Pediatrics, Women's and Children's Hospital, Chongqing Medical University, 120 Longshan Rd., Chongqing, 401147, People's Republic of China
- Department of Pediatrics, Children's Hospital, Chongqing Medical University, Chongqing, People's Republic of China
- Department of Pediatric General Surgery, Chongqing Maternal and Child Health Hospital, Chongqing Medical University, Chongqing, People's Republic of China
| | - Q Liu
- Department of Pediatrics, Women's and Children's Hospital, Chongqing Medical University, 120 Longshan Rd., Chongqing, 401147, People's Republic of China
- Department of Fetus and Pediatrics, Chongqing Health Center for Women and Children, Chongqing, People's Republic of China
| | - G Chen
- Department of Pediatrics, Women's and Children's Hospital, Chongqing Medical University, 120 Longshan Rd., Chongqing, 401147, People's Republic of China
- Department of Fetus and Pediatrics, Chongqing Health Center for Women and Children, Chongqing, People's Republic of China
| | - B Xia
- Department of Pediatrics, Women's and Children's Hospital, Chongqing Medical University, 120 Longshan Rd., Chongqing, 401147, People's Republic of China
- Department of Fetus and Pediatrics, Chongqing Health Center for Women and Children, Chongqing, People's Republic of China
| | - D Zeng
- Department of Pediatrics, Women's and Children's Hospital, Chongqing Medical University, 120 Longshan Rd., Chongqing, 401147, People's Republic of China
- Department of Fetus and Pediatrics, Chongqing Health Center for Women and Children, Chongqing, People's Republic of China
| | - G Chen
- Department of Pediatrics, Women's and Children's Hospital, Chongqing Medical University, 120 Longshan Rd., Chongqing, 401147, People's Republic of China.
- Department of Fetus and Pediatrics, Chongqing Health Center for Women and Children, Chongqing, People's Republic of China.
- Department of Pediatric General Surgery, Chongqing Maternal and Child Health Hospital, Chongqing Medical University, Chongqing, People's Republic of China.
- Department of Obstetrics and Gynecology, Chongqing Health Center for Women and Children, Women and Children's Hospital of Chongqing Medical University, 120 Longshan Rd., Chongqing, 401147, People's Republic of China.
| | - C Guo
- Department of Pediatrics, Women's and Children's Hospital, Chongqing Medical University, 120 Longshan Rd., Chongqing, 401147, People's Republic of China.
- Department of Fetus and Pediatrics, Chongqing Health Center for Women and Children, Chongqing, People's Republic of China.
- Department of Pediatric General Surgery, Chongqing Maternal and Child Health Hospital, Chongqing Medical University, Chongqing, People's Republic of China.
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17
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Tian Y, Shepherd BE, Li C, Zeng D, Schildcrout JS. Analyzing clustered continuous response variables with ordinal regression models. Biometrics 2023; 79:3764-3777. [PMID: 37459181 PMCID: PMC10792095 DOI: 10.1111/biom.13904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Accepted: 06/28/2023] [Indexed: 12/21/2023]
Abstract
Continuous response data are regularly transformed to meet regression modeling assumptions. However, approaches taken to identify the appropriate transformation can be ad hoc and can increase model uncertainty. Further, the resulting transformations often vary across studies leading to difficulties with synthesizing and interpreting results. When a continuous response variable is measured repeatedly within individuals or when continuous responses arise from clusters, analyses have the additional challenge caused by within-individual or within-cluster correlations. We extend a widely used ordinal regression model, the cumulative probability model (CPM), to fit clustered, continuous response data using generalized estimating equations for ordinal responses. With the proposed approach, estimates of marginal model parameters, cumulative distribution functions , expectations, and quantiles conditional on covariates can be obtained without pretransformation of the response data. While computational challenges arise with large numbers of distinct values of the continuous response variable, we propose feasible and computationally efficient approaches to fit CPMs under commonly used working correlation structures. We study finite sample operating characteristics of the estimators via simulation and illustrate their implementation with two data examples. One studies predictors of CD4:CD8 ratios in a cohort living with HIV, and the other investigates the association of a single nucleotide polymorphism and lung function decline in a cohort with early chronic obstructive pulmonary disease.
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Affiliation(s)
- Yuqi Tian
- Department of Biostatistics, Vanderbilt University, Nashville, Tennessee
| | - Bryan E. Shepherd
- Department of Biostatistics, Vanderbilt University, Nashville, Tennessee
| | - Chun Li
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
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18
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Wong SA, Lebois LAM, Ely TD, van Rooij SJH, Bruce SE, Murty VP, Jovanovic T, House SL, Beaudoin FL, An X, Zeng D, Neylan TC, Clifford GD, Linnstaedt SD, Germine LT, Bollen KA, Rauch SL, Haran JP, Storrow AB, Lewandowski C, Musey PI, Hendry PL, Sheikh S, Jones CW, Punches BE, Kurz MC, Swor RA, Hudak LA, Pascual JL, Seamon MJ, Pearson C, Peak DA, Merchant RC, Domeier RM, Rathlev NK, O'Neil BJ, Sergot P, Sanchez LD, Miller MW, Pietrzak RH, Joormann J, Barch DM, Pizzagalli DA, Harte SE, Elliott JM, Kessler RC, Koenen KC, McLean SA, Ressler KJ, Stevens JS, Harnett NG. Internal capsule microstructure mediates the relationship between childhood maltreatment and PTSD following adulthood trauma exposure. Mol Psychiatry 2023; 28:5140-5149. [PMID: 36932158 PMCID: PMC10505244 DOI: 10.1038/s41380-023-02012-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 02/17/2023] [Accepted: 02/22/2023] [Indexed: 03/19/2023]
Abstract
Childhood trauma is a known risk factor for trauma and stress-related disorders in adulthood. However, limited research has investigated the impact of childhood trauma on brain structure linked to later posttraumatic dysfunction. We investigated the effect of childhood trauma on white matter microstructure after recent trauma and its relationship with future posttraumatic dysfunction among trauma-exposed adult participants (n = 202) recruited from emergency departments as part of the AURORA Study. Participants completed self-report scales assessing prior childhood maltreatment within 2-weeks in addition to assessments of PTSD, depression, anxiety, and dissociation symptoms within 6-months of their traumatic event. Fractional anisotropy (FA) obtained from diffusion tensor imaging (DTI) collected at 2-weeks and 6-months was used to index white matter microstructure. Childhood maltreatment load predicted 6-month PTSD symptoms (b = 1.75, SE = 0.78, 95% CI = [0.20, 3.29]) and inversely varied with FA in the bilateral internal capsule (IC) at 2-weeks (p = 0.0294, FDR corrected) and 6-months (p = 0.0238, FDR corrected). We observed a significant indirect effect of childhood maltreatment load on 6-month PTSD symptoms through 2-week IC microstructure (b = 0.37, Boot SE = 0.18, 95% CI = [0.05, 0.76]) that fully mediated the effect of childhood maltreatment load on PCL-5 scores (b = 1.37, SE = 0.79, 95% CI = [-0.18, 2.93]). IC microstructure did not mediate relationships between childhood maltreatment and depressive, anxiety, or dissociative symptomatology. Our findings suggest a unique role for IC microstructure as a stable neural pathway between childhood trauma and future PTSD symptoms following recent trauma. Notably, our work did not support roles of white matter tracts previously found to vary with PTSD symptoms and childhood trauma exposure, including the cingulum bundle, uncinate fasciculus, and corpus callosum. Given the IC contains sensory fibers linked to perception and motor control, childhood maltreatment might impact the neural circuits that relay and process threat-related inputs and responses to trauma.
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Affiliation(s)
- Samantha A Wong
- Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA
| | - Lauren A M Lebois
- Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Timothy D Ely
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Sanne J H van Rooij
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Steven E Bruce
- Department of Psychological Sciences, University of Missouri-St. Louis, St. Louis, MO, USA
| | - Vishnu P Murty
- Department of Psychology, Temple University, Philadelphia, PA, USA
| | - Tanja Jovanovic
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, MI, USA
| | - Stacey L House
- Department of Emergency Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Francesca L Beaudoin
- Department of Epidemiology, Brown University, Providence, RI, USA
- Department of Emergency Medicine, Brown University, Providence, RI, USA
| | - Xinming An
- Institute for Trauma Recovery, Department of Anesthesiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Donglin Zeng
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Thomas C Neylan
- Departments of Psychiatry and Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Gari D Clifford
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Sarah D Linnstaedt
- Institute for Trauma Recovery, Department of Anesthesiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Laura T Germine
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA
- The Many Brains Project, Belmont, MA, USA
| | - Kenneth A Bollen
- Department of Psychology and Neuroscience & Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Scott L Rauch
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, McLean Hospital, Belmont, MA, USA
| | - John P Haran
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Alan B Storrow
- Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Paul I Musey
- Department of Emergency Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Phyllis L Hendry
- Department of Emergency Medicine, University of Florida College of Medicine-Jacksonville, Jacksonville, FL, USA
| | - Sophia Sheikh
- Department of Emergency Medicine, University of Florida College of Medicine-Jacksonville, Jacksonville, FL, USA
| | - Christopher W Jones
- Department of Emergency Medicine, Cooper Medical School of Rowan University, Camden, NJ, USA
| | - Brittany E Punches
- Department of Emergency Medicine, Ohio State University College of Medicine, Columbus, OH, USA
- Ohio State University College of Nursing, Columbus, OH, USA
| | - Michael C Kurz
- Department of Emergency Medicine, University of Alabama School of Medicine, Birmingham, AL, USA
- Department of Surgery, Division of Acute Care Surgery, University of Alabama School of Medicine, Birmingham, AL, USA
- Center for Injury Science, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Robert A Swor
- Department of Emergency Medicine, Oakland University William Beaumont School of Medicine, Rochester, MI, USA
| | - Lauren A Hudak
- Department of Emergency Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Jose L Pascual
- Department of Surgery, Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mark J Seamon
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Surgery, Division of Traumatology, Surgical Critical Care and Emergency Surgery, University of Pennsylvania, Philadelphia, PA, USA
| | - Claire Pearson
- Department of Emergency Medicine, Wayne State University, Ascension St. John Hospital, Detroit, MI, USA
| | - David A Peak
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Roland C Merchant
- Department of Emergency Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Robert M Domeier
- Department of Emergency Medicine, Saint Joseph Mercy Hospital, Ypsilanti, MI, USA
| | - Niels K Rathlev
- Department of Emergency Medicine, University of Massachusetts Medical School-Baystate, Springfield, MA, USA
| | - Brian J O'Neil
- Department of Emergency Medicine, Wayne State University, Detroit Receiving Hospital, Detroit, MI, USA
| | - Paulina Sergot
- Department of Emergency Medicine, McGovern Medical School at UTHealth, Houston, TX, USA
| | - Leon D Sanchez
- Department of Emergency Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Emergency Medicine, Harvard Medical School, Boston, MA, USA
| | - Mark W Miller
- National Center for PTSD, Behavioral Science Division, VA Boston Healthcare System, Boston, MA, USA
- Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
| | - Robert H Pietrzak
- National Center for PTSD, Clinical Neurosciences Division, VA Connecticut Healthcare System, West Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Jutta Joormann
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Deanna M Barch
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Diego A Pizzagalli
- Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Steven E Harte
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, USA
- Department of Internal Medicine-Rheumatology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - James M Elliott
- Kolling Institute, University of Sydney, St Leonards, NSW, Australia
- Faculty of Medicine and Health, University of Sydney, Northern Sydney Local Health District, Camperdown, NSW, Australia
- Physical Therapy & Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Ronald C Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Karestan C Koenen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Samuel A McLean
- Department of Emergency Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Institute for Trauma Recovery, Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kerry J Ressler
- Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Jennifer S Stevens
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA.
| | - Nathaniel G Harnett
- Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA.
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
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19
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Guo X, Zeng D, Wang Y. A Semiparametric Inverse Reinforcement Learning Approach to Characterize Decision Making for Mental Disorders. J Am Stat Assoc 2023; 119:27-38. [PMID: 38706706 PMCID: PMC11068237 DOI: 10.1080/01621459.2023.2261184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Accepted: 09/03/2023] [Indexed: 05/07/2024]
Abstract
Major depressive disorder (MDD) is one of the leading causes of disability-adjusted life years. Emerging evidence indicates the presence of reward processing abnormalities in MDD. An important scientific question is whether the abnormalities are due to reduced sensitivity to received rewards or reduced learning ability. Motivated by the probabilistic reward task (PRT) experiment in the EMBARC study, we propose a semiparametric inverse reinforcement learning (RL) approach to characterize the reward-based decision-making of MDD patients. The model assumes that a subject's decision-making process is updated based on a reward prediction error weighted by the subject-specific learning rate. To account for the fact that one favors a decision leading to a potentially high reward, but this decision process is not necessarily linear, we model reward sensitivity with a non-decreasing and nonlinear function. For inference, we estimate the latter via approximation by I-splines and then maximize the joint conditional log-likelihood. We show that the resulting estimators are consistent and asymptotically normal. Through extensive simulation studies, we demonstrate that under different reward-generating distributions, the semiparametric inverse RL outperforms the parametric inverse RL. We apply the proposed method to EMBARC and find that MDD and control groups have similar learning rates but different reward sensitivity functions. There is strong statistical evidence that reward sensitivity functions have nonlinear forms. Using additional brain imaging data in the same study, we find that both reward sensitivity and learning rate are associated with brain activities in the negative affect circuitry under an emotional conflict task.
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Affiliation(s)
- Xingche Guo
- Department of Biostatistics, Columbia University
| | - Donglin Zeng
- Department of Biostatistics, University of Michigan
| | - Yuanjia Wang
- Departments of Biostatistics and Psychiatry, Columbia University
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20
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Zeamer AL, Salive MC, An X, Beaudoin FL, House SL, Stevens JS, Zeng D, Neylan TC, Clifford GD, Linnstaedt SD, Rauch SL, Storrow AB, Lewandowski C, Musey PI, Hendry PL, Sheikh S, Jones CW, Punches BE, Swor RA, Hudak LA, Pascual JL, Seamon MJ, Harris E, Pearson C, Peak DA, Merchant RC, Domeier RM, Rathlev NK, O'Neil BJ, Sergot P, Sanchez LD, Bruce SE, Kessler RC, Koenen KC, McLean SA, Bucci V, Haran JP. Association between microbiome and the development of adverse posttraumatic neuropsychiatric sequelae after traumatic stress exposure. Transl Psychiatry 2023; 13:354. [PMID: 37980332 PMCID: PMC10657470 DOI: 10.1038/s41398-023-02643-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 10/20/2023] [Accepted: 10/30/2023] [Indexed: 11/20/2023] Open
Abstract
Patients exposed to trauma often experience high rates of adverse post-traumatic neuropsychiatric sequelae (APNS). The biological mechanisms promoting APNS are currently unknown, but the microbiota-gut-brain axis offers an avenue to understanding mechanisms as well as possibilities for intervention. Microbiome composition after trauma exposure has been poorly examined regarding neuropsychiatric outcomes. We aimed to determine whether the gut microbiomes of trauma-exposed emergency department patients who develop APNS have dysfunctional gut microbiome profiles and discover potential associated mechanisms. We performed metagenomic analysis on stool samples (n = 51) from a subset of adults enrolled in the Advancing Understanding of RecOvery afteR traumA (AURORA) study. Two-, eight- and twelve-week post-trauma outcomes for post-traumatic stress disorder (PTSD) (PTSD checklist for DSM-5), normalized depression scores (PROMIS Depression Short Form 8b) and somatic symptom counts were collected. Generalized linear models were created for each outcome using microbial abundances and relevant demographics. Mixed-effect random forest machine learning models were used to identify associations between APNS outcomes and microbial features and encoded metabolic pathways from stool metagenomics. Microbial species, including Flavonifractor plautii, Ruminococcus gnavus and, Bifidobacterium species, which are prevalent commensal gut microbes, were found to be important in predicting worse APNS outcomes from microbial abundance data. Notably, through APNS outcome modeling using microbial metabolic pathways, worse APNS outcomes were highly predicted by decreased L-arginine related pathway genes and increased citrulline and ornithine pathways. Common commensal microbial species are enriched in individuals who develop APNS. More notably, we identified a biological mechanism through which the gut microbiome reduces global arginine bioavailability, a metabolic change that has also been demonstrated in the plasma of patients with PTSD.
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Affiliation(s)
- Abigail L Zeamer
- Department of Microbiology and Physiologic Systems, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Marie-Claire Salive
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Xinming An
- Institute for Trauma Recovery, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Francesca L Beaudoin
- Department of Epidemiology, Brown University, Providence, RI, USA
- Department of Emergency Medicine, Brown University, Providence, RI, USA
| | - Stacey L House
- Department of Emergency Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Jennifer S Stevens
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Donglin Zeng
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Thomas C Neylan
- Departments of Psychiatry and Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Gari D Clifford
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Sarah D Linnstaedt
- Institute for Trauma Recovery, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- The Many Brains Project, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Scott L Rauch
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, McLean Hospital, Belmont, MA, USA
| | - Alan B Storrow
- Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Paul I Musey
- Department of Emergency Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Phyllis L Hendry
- Department of Emergency Medicine, University of Florida College of Medicine-Jacksonville, Jacksonville, FL, USA
| | - Sophia Sheikh
- Department of Emergency Medicine, University of Florida College of Medicine-Jacksonville, Jacksonville, FL, USA
| | - Christopher W Jones
- Department of Emergency Medicine, Cooper Medical School of Rowan University, Camden, NJ, USA
| | - Brittany E Punches
- Department of Emergency Medicine, Ohio State University College of Medicine, Columbus, OH, USA
- Ohio State University College of Nursing, Columbus, OH, USA
| | - Robert A Swor
- Department of Emergency Medicine, Oakland University William Beaumont School of Medicine, Rochester, MI, USA
| | - Lauren A Hudak
- Department of Emergency Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Jose L Pascual
- Department of Surgery, University of Pennsylvania, Philadelphia, PA, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mark J Seamon
- Department of Surgery, University of Pennsylvania, Philadelphia, PA, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Erica Harris
- Department of Emergency Medicine, Einstein Medical Center, Philadelphia, PA, USA
| | - Claire Pearson
- Department of Emergency Medicine, Wayne State University, Ascension St. John Hospital, Detroit, MI, USA
| | - David A Peak
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Roland C Merchant
- Department of Emergency Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Robert M Domeier
- Department of Emergency Medicine, Trinity Health-Ann Arbor, Ypsilanti, MI, USA
| | - Niels K Rathlev
- Department of Emergency Medicine, University of Massachusetts Medical School-Baystate, Springfield, MA, USA
| | - Brian J O'Neil
- Department of Emergency Medicine, Wayne State University, Detroit Receiving Hospital, Detroit, MI, USA
| | - Paulina Sergot
- Department of Emergency Medicine, McGovern Medical School at UTHealth, Houston, TX, USA
| | - Leon D Sanchez
- Department of Emergency Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Emergency Medicine, Harvard Medical School, Boston, MA, USA
| | - Steven E Bruce
- Department of Psychological Sciences, University of Missouri - St. Louis, St. Louis, MO, USA
| | - Ronald C Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | | | - Samuel A McLean
- Department of Emergency Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Vanni Bucci
- Department of Microbiology and Physiologic Systems, University of Massachusetts Chan Medical School, Worcester, MA, USA.
- Program in Microbiome Dynamics, University of Massachusetts Chan Medical School, Worcester, MA, USA.
| | - John P Haran
- Department of Microbiology and Physiologic Systems, University of Massachusetts Chan Medical School, Worcester, MA, USA.
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA.
- Program in Microbiome Dynamics, University of Massachusetts Chan Medical School, Worcester, MA, USA.
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21
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Lin DY, Xu Y, Zeng D, Sunny SK. A Cost-Benefit Analysis of Bivalent Covid-19 Vaccines. J Biotechnol Biomed 2023; 6:551-553. [PMID: 38050633 PMCID: PMC10695403 DOI: 10.26502/jbb.2642-91280116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/06/2023]
Affiliation(s)
- Dan-Yu Lin
- The University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC
| | - Yangjianchen Xu
- The University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC
| | - Donglin Zeng
- The University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC
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22
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Lin DY, Xu Y, Gu Y, Zeng D, Wheeler B, Young H, Moore Z, Sunny SK. Effects of COVID-19 vaccination and previous SARS-CoV-2 infection on omicron infection and severe outcomes in children under 12 years of age in the USA: an observational cohort study. Lancet Infect Dis 2023; 23:1257-1265. [PMID: 37336222 PMCID: PMC10275621 DOI: 10.1016/s1473-3099(23)00272-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 03/22/2023] [Accepted: 04/14/2023] [Indexed: 06/21/2023]
Abstract
BACKGROUND Data on the protection conferred by COVID-19 vaccination and previous SARS-CoV-2 infection against omicron (B.1.1.529) infection in young children are scarce. We aimed to estimate the time-varying effects of primary and booster COVID-19 vaccination and previous SARS-CoV-2 infection on subsequent omicron infection and severe illness (hospital admission or death) in children younger than 12 years of age. METHODS In this observational cohort study, we obtained individual-level records on vaccination with the BNT162b2 and mRNA-1273 vaccines and clinical outcomes from the North Carolina COVID-19 Surveillance System and the COVID-19 Vaccine Management System for 1 368 721 North Carolina residents aged 11 years or younger from Oct 29, 2021 (Oct 29, 2021 for children aged 5-11 years and June 17, 2022 for children aged 0-4 years), to Jan 6, 2023. We used Cox regression to estimate the time-varying effects of primary and booster vaccination and previous infection on the risks of omicron infection, hospital admission, and death. FINDINGS For children 5-11 years of age, the effectiveness of primary vaccination against infection, compared with being unvaccinated, was 59·9% (95% CI 58·5-61·2) at 1 month, 33·7% (32·6-34·8) at 4 months, and 14·9% (95% CI 12·3-17·5) at 10 months after the first dose. Compared with primary vaccination only, the effectiveness of a monovalent booster dose after 1 month was 24·4% (14·4-33·2) and that of a bivalent booster dose was 76·7% (45·7-90·0). The effectiveness of omicron infection against reinfection was 79·9% (78·8-80·9) after 3 months and 53·9% (52·3-55·5) after 6 months. For children 0-4 years of age, the effectiveness of primary vaccination against infection, compared with being unvaccinated, was 63·8% (57·0-69·5) at 2 months and 58·1% (48·3-66·1) at 5 months after the first dose, and the effectiveness of omicron infection against reinfection was 77·3% (75·9-78·6) after 3 months and 64·7% (63·3-66·1) after 6 months. For both age groups, vaccination and previous infection had better effectiveness against severe illness as measured by hospital admission or death as a composite endpoint than against infection. INTERPRETATION The BNT162b2 and mRNA-1273 vaccines were effective against omicron infection and severe outcomes in children younger than 12 years, although the effectiveness decreased over time. Bivalent boosters were more effective than monovalent boosters. Immunity acquired via omicron infection was high and waned gradually over time. These findings can be used to develop effective prevention strategies against COVID-19 in children younger than 12 years. FUNDING US National Institutes of Health.
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Affiliation(s)
- Dan-Yu Lin
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Yangjianchen Xu
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yu Gu
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Donglin Zeng
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Bradford Wheeler
- North Carolina Department of Health and Human Services, Raleigh, NC, USA
| | - Hayley Young
- North Carolina Department of Health and Human Services, Raleigh, NC, USA
| | - Zack Moore
- North Carolina Department of Health and Human Services, Raleigh, NC, USA
| | - Shadia K Sunny
- Centers for Disease Control and Prevention Foundation at North Carolina Department of Health and Human Services, Raleigh, NC, USA
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23
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Tu S, Li C, Zeng D, Shepherd BE. Rank intraclass correlation for clustered data. Stat Med 2023; 42:4333-4348. [PMID: 37548059 PMCID: PMC10592008 DOI: 10.1002/sim.9864] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 06/02/2023] [Accepted: 07/17/2023] [Indexed: 08/08/2023]
Abstract
Clustered data are common in biomedical research. Observations in the same cluster are often more similar to each other than to observations from other clusters. The intraclass correlation coefficient (ICC), first introduced by R. A. Fisher, is frequently used to measure this degree of similarity. However, the ICC is sensitive to extreme values and skewed distributions, and depends on the scale of the data. It is also not applicable to ordered categorical data. We define the rank ICC as a natural extension of Fisher's ICC to the rank scale, and describe its corresponding population parameter. The rank ICC is simply interpreted as the rank correlation between a random pair of observations from the same cluster. We also extend the definition when the underlying distribution has more than two hierarchies. We describe estimation and inference procedures, show the asymptotic properties of our estimator, conduct simulations to evaluate its performance, and illustrate our method in three real data examples with skewed data, count data, and three-level ordered categorical data.
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Affiliation(s)
- Shengxin Tu
- Department of Biostatistics, Vanderbilt University, Nashville, Tennessee, USA
| | - Chun Li
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California, USA
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Bryan E. Shepherd
- Department of Biostatistics, Vanderbilt University, Nashville, Tennessee, USA
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24
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Zou H, Zeng D, Xiao L, Luo S. BAYESIAN INFERENCE AND DYNAMIC PREDICTION FOR MULTIVARIATE LONGITUDINAL AND SURVIVAL DATA. Ann Appl Stat 2023; 17:2574-2595. [PMID: 37719893 PMCID: PMC10500582 DOI: 10.1214/23-aoas1733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
Alzheimer's disease (AD) is a complex neurological disorder impairing multiple domains such as cognition and daily functions. To better understand the disease and its progression, many AD research studies collect multiple longitudinal outcomes that are strongly predictive of the onset of AD dementia. We propose a joint model based on a multivariate functional mixed model framework (referred to as MFMM-JM) that simultaneously models the multiple longitudinal outcomes and the time to dementia onset. We develop six functional forms to fully investigate the complex association between longitudinal outcomes and dementia onset. Moreover, we use the Bayesian methods for statistical inference and develop a dynamic prediction framework that provides accurate personalized predictions of disease progressions based on new subject-specific data. We apply the proposed MFMM-JM to two large ongoing AD studies: the Alzheimer's Disease Neuroimaging Initiative (ADNI) and National Alzheimer's Coordinating Center (NACC), and identify the functional forms with the best predictive performance. our method is also validated by extensive simulation studies with five settings.
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Affiliation(s)
- Haotian Zou
- Department of Biostatistics, University of North Carolina at Chapel Hill
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina at Chapel Hill
| | - Luo Xiao
- Department of Statistics, North Carolina State University
| | - Sheng Luo
- Department of Biostatistics and Bioinformatics, Duke University
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25
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Xu Y, Zeng D, Lin DY. Marginal proportional hazards models for multivariate interval-censored data. Biometrika 2023; 110:815-830. [PMID: 37601305 PMCID: PMC10434824 DOI: 10.1093/biomet/asac059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/22/2023] Open
Abstract
Multivariate interval-censored data arise when there are multiple types of events or clusters of study subjects, such that the event times are potentially correlated and when each event is only known to occur over a particular time interval. We formulate the effects of potentially time-varying covariates on the multivariate event times through marginal proportional hazards models while leaving the dependence structures of the related event times unspecified. We construct the nonparametric pseudolikelihood under the working assumption that all event times are independent, and we provide a simple and stable EM-type algorithm. The resulting nonparametric maximum pseudolikelihood estimators for the regression parameters are shown to be consistent and asymptotically normal, with a limiting covariance matrix that can be consistently estimated by a sandwich estimator under arbitrary dependence structures for the related event times. We evaluate the performance of the proposed methods through extensive simulation studies and present an application to data from the Atherosclerosis Risk in Communities Study.
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Affiliation(s)
- Yangjianchen Xu
- Department of Biostatistics, University of North Carolina, 3101E McGavran-Greenberg Hall, Chapel Hill, North Carolina 27599, U.S.A
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina, 3101E McGavran-Greenberg Hall, Chapel Hill, North Carolina 27599, U.S.A
| | - D Y Lin
- Department of Biostatistics, University of North Carolina, 3101E McGavran-Greenberg Hall, Chapel Hill, North Carolina 27599, U.S.A
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26
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Xu T, Chen Y, Zeng D, Wang Y. Mixed-Response State-Space Model for Analyzing Multi-Dimensional Digital Phenotypes. J Am Stat Assoc 2023; 118:2288-2300. [PMID: 38404670 PMCID: PMC10888145 DOI: 10.1080/01621459.2023.2225742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 06/01/2023] [Indexed: 02/27/2024]
Abstract
Digital technologies (e.g., mobile phones) can be used to obtain objective, frequent, and real-world digital phenotypes from individuals. However, modeling these data poses substantial challenges since observational data are subject to confounding and various sources of variabilities. For example, signals on patients' underlying health status and treatment effects are mixed with variation due to the living environment and measurement noises. The digital phenotype data thus shows extensive variabilities between- and within-patient as well as across different health domains (e.g., motor, cognitive, and speaking). Motivated by a mobile health study of Parkinson's disease (PD), we develop a mixed-response state-space (MRSS) model to jointly capture multi-dimensional, multi-modal digital phenotypes and their measurement processes by a finite number of latent state time series. These latent states reflect the dynamic health status and personalized time-varying treatment effects and can be used to adjust for informative measurements. For computation, we use the Kalman filter for Gaussian phenotypes and importance sampling with Laplace approximation for non-Gaussian phenotypes. We conduct comprehensive simulation studies and demonstrate the advantage of MRSS in modeling a mobile health study that remotely collects real-time digital phenotypes from PD patients.
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Affiliation(s)
- Tianchen Xu
- Department of Biostatistics Mailman School of Public Health, Columbia University, NY 10032, USA
| | - Yuan Chen
- Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center, NY 10065, USA
| | - Donglin Zeng
- Department of Biostatistics The University of North Carolina at Chapel Hill, NC 27599, USA
| | - Yuanjia Wang
- Department of Biostatistics Mailman School of Public Health, Columbia University, NY 10032, USA
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27
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Harnett NG, Fani N, Carter S, Sanchez LD, Rowland GE, Davie WM, Guzman C, Lebois LAM, Ely TD, van Rooij SJH, Seligowski AV, Winters S, Grasser LR, Musey PI, Seamon MJ, House SL, Beaudoin FL, An X, Zeng D, Neylan TC, Clifford GD, Linnstaedt SD, Germine LT, Bollen KA, Rauch SL, Haran JP, Storrow AB, Lewandowski C, Hendry PL, Sheikh S, Jones CW, Punches BE, Swor RA, Hudak LA, Pascual JL, Harris E, Chang AM, Pearson C, Peak DA, Merchant RC, Domeier RM, Rathlev NK, Bruce SE, Miller MW, Pietrzak RH, Joormann J, Barch DM, Pizzagalli DA, Harte SE, Elliott JM, Kessler RC, Koenen KC, McLean SA, Jovanovic T, Stevens JS, Ressler KJ. Structural inequities contribute to racial/ethnic differences in neurophysiological tone, but not threat reactivity, after trauma exposure. Mol Psychiatry 2023; 28:2975-2984. [PMID: 36725899 PMCID: PMC10615735 DOI: 10.1038/s41380-023-01971-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 01/13/2023] [Accepted: 01/17/2023] [Indexed: 02/03/2023]
Abstract
Considerable racial/ethnic disparities persist in exposure to life stressors and socioeconomic resources that can directly affect threat neurocircuitry, particularly the amygdala, that partially mediates susceptibility to adverse posttraumatic outcomes. Limited work to date, however, has investigated potential racial/ethnic variability in amygdala reactivity or connectivity that may in turn be related to outcomes such as post-traumatic stress disorder (PTSD). Participants from the AURORA study (n = 283), a multisite longitudinal study of trauma outcomes, completed functional magnetic resonance imaging and psychophysiology within approximately two-weeks of trauma exposure. Seed-based amygdala connectivity and amygdala reactivity during passive viewing of fearful and neutral faces were assessed during fMRI. Physiological activity was assessed during Pavlovian threat conditioning. Participants also reported the severity of posttraumatic symptoms 3 and 6 months after trauma. Black individuals showed lower baseline skin conductance levels and startle compared to White individuals, but no differences were observed in physiological reactions to threat. Further, Hispanic and Black participants showed greater amygdala connectivity to regions including the dorsolateral prefrontal cortex (PFC), dorsal anterior cingulate cortex, insula, and cerebellum compared to White participants. No differences were observed in amygdala reactivity to threat. Amygdala connectivity was associated with 3-month PTSD symptoms, but the associations differed by racial/ethnic group and were partly driven by group differences in structural inequities. The present findings suggest variability in tonic neurophysiological arousal in the early aftermath of trauma between racial/ethnic groups, driven by structural inequality, impacts neural processes that mediate susceptibility to later PTSD symptoms.
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Affiliation(s)
- Nathaniel G Harnett
- Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA.
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
| | - Negar Fani
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Sierra Carter
- Department of Psychology, Georgia State University, Atlanta, GA, USA
| | - Leon D Sanchez
- Department of Emergency Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Department of Emergency Medicine, Harvard Medical School, Boston, MA, USA
| | - Grace E Rowland
- Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA
| | - William M Davie
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, MI, USA
| | - Camilo Guzman
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, MI, USA
- Department of Psychiatry, Henry Ford Health System, Detroit, MI, USA
| | - Lauren A M Lebois
- Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Timothy D Ely
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Sanne J H van Rooij
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Antonia V Seligowski
- Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Sterling Winters
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, MI, USA
| | - Lana R Grasser
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, MI, USA
| | - Paul I Musey
- Department of Emergency Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Mark J Seamon
- Department of Surgery, Division of Traumatology, Surgical Critical Care and Emergency Surgery, University of Pennsylvania, Philadelphia, PA, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Stacey L House
- Department of Emergency Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Francesca L Beaudoin
- Department of Epidemiology, Brown University School of Public Health, Providence, RI, USA
| | - Xinming An
- Institute for Trauma Recovery, Department of Anesthesiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Donglin Zeng
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Thomas C Neylan
- Departments of Psychiatry and Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Gari D Clifford
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Sarah D Linnstaedt
- Institute for Trauma Recovery, Department of Anesthesiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Laura T Germine
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA
- The Many Brains Project, Belmont, MA, USA
| | - Kenneth A Bollen
- Department of Psychology and Neuroscience & Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Scott L Rauch
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, McLean Hospital, Belmont, MA, USA
| | - John P Haran
- Department of Emergency Medicine, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Alan B Storrow
- Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Phyllis L Hendry
- Department of Emergency Medicine, University of Florida College of Medicine -Jacksonville, Jacksonville, FL, USA
| | - Sophia Sheikh
- Department of Emergency Medicine, University of Florida College of Medicine -Jacksonville, Jacksonville, FL, USA
| | - Christopher W Jones
- Department of Emergency Medicine, Cooper Medical School of Rowan University, Camden, NJ, USA
| | - Brittany E Punches
- Department of Emergency Medicine, Ohio State University College of Medicine, Columbus, OH, USA
- Ohio State University College of Nursing, Columbus, OH, USA
| | - Robert A Swor
- Department of Emergency Medicine, Oakland University William Beaumont School of Medicine, Rochester, MI, USA
| | - Lauren A Hudak
- Department of Emergency Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Jose L Pascual
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Surgery, Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Anna M Chang
- Department of Emergency Medicine, Jefferson University Hospitals, Philadelphia, PA, USA
| | - Claire Pearson
- Department of Emergency Medicine, Wayne State University, Ascension St. John Hospital, Detroit, MI, USA
| | - David A Peak
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Roland C Merchant
- Department of Emergency Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Robert M Domeier
- Department of Emergency Medicine, Saint Joseph Mercy Hospital, Ypsilanti, MI, USA
| | - Niels K Rathlev
- Department of Emergency Medicine, University of Massachusetts Medical School-Baystate, Springfield, MA, USA
| | - Steven E Bruce
- Department of Psychological Sciences, University of Missouri - St. Louis, St. Louis, MO, USA
| | - Mark W Miller
- National Center for PTSD, Behavioral Science Division, VA Boston Healthcare System, Boston, MA, USA
- Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
| | - Robert H Pietrzak
- National Center for PTSD, Clinical Neurosciences Division, VA Connecticut Healthcare System, West Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Jutta Joormann
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Deanna M Barch
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Diego A Pizzagalli
- Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Steven E Harte
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, USA
- Department of Internal Medicine-Rheumatology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - James M Elliott
- Kolling Institute, University of Sydney, St Leonards, New South Wales, Australia
- Faculty of Medicine and Health, University of Sydney, Northern Sydney Local Health District, St Leonards, New South Wales, Australia
- Physical Therapy & Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Ronald C Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | - Karestan C Koenen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Samuel A McLean
- Department of Emergency Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Institute for Trauma Recovery, Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tanja Jovanovic
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, MI, USA
| | - Jennifer S Stevens
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Kerry J Ressler
- Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
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28
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Yang S, Gao C, Zeng D, Wang X. Elastic integrative analysis of randomised trial and real-world data for treatment heterogeneity estimation. J R Stat Soc Series B Stat Methodol 2023; 85:575-596. [PMID: 37521165 PMCID: PMC10376438 DOI: 10.1093/jrsssb/qkad017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 05/14/2022] [Accepted: 02/28/2023] [Indexed: 08/01/2023]
Abstract
We propose a test-based elastic integrative analysis of the randomised trial and real-world data to estimate treatment effect heterogeneity with a vector of known effect modifiers. When the real-world data are not subject to bias, our approach combines the trial and real-world data for efficient estimation. Utilising the trial design, we construct a test to decide whether or not to use real-world data. We characterise the asymptotic distribution of the test-based estimator under local alternatives. We provide a data-adaptive procedure to select the test threshold that promises the smallest mean square error and an elastic confidence interval with a good finite-sample coverage property.
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Affiliation(s)
- Shu Yang
- Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA
| | - Chenyin Gao
- Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Xiaofei Wang
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
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29
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Zhou H, Zeng D, Bian Z, Ma J. [A semi-supervised network-based tissue-aware contrast enhancement method for CT images]. Nan Fang Yi Ke Da Xue Xue Bao 2023; 43:985-993. [PMID: 37439171 DOI: 10.12122/j.issn.1673-4254.2023.06.14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 07/14/2023]
Abstract
OBJECTIVE To propose a tissue- aware contrast enhancement network (T- ACEnet) for CT image enhancement and validate its accuracy in CT image organ segmentation tasks. METHODS The original CT images were mapped to generate low dynamic grayscale images with lung and soft tissue window contrasts, and the supervised sub-network learned to recognize the optimal window width and level setting of the lung and abdominal soft tissues via the lung mask. The self-supervised sub-network then used the extreme value suppression loss function to preserve more organ edge structure information. The images generated by the T-ACEnet were fed into the segmentation network to segment multiple abdominal organs. RESULTS The images obtained by T-ACEnet were capable of providing more window setting information in a single image, which allowed the physicians to conduct preliminary screening of the lesions. Compared with the suboptimal methods, T-ACE images achieved improvements by 0.51, 0.26, 0.10, and 14.14 in SSIM, QABF, VIFF, and PSNR metrics, respectively, with a reduced MSE by an order of magnitude. When T-ACE images were used as input for segmentation networks, the organ segmentation accuracy could be effectively improved without changing the model as compared with the original CT images. All the 5 segmentation quantitative indices were improved, with the maximum improvement of 4.16%. CONCLUSION The T-ACEnet can perceptually improve the contrast of organ tissues and provide more comprehensive and continuous diagnostic information, and the T-ACE images generated using this method can significantly improve the performance of organ segmentation tasks.
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Affiliation(s)
- H Zhou
- Department of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
- Pazhou Lab (Huangpu), Guangzhou 510515, China
| | - D Zeng
- Department of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
- Pazhou Lab (Huangpu), Guangzhou 510515, China
| | - Z Bian
- Department of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
- Pazhou Lab (Huangpu), Guangzhou 510515, China
| | - J Ma
- Department of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
- Pazhou Lab (Huangpu), Guangzhou 510515, China
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30
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Yu H, Wang Y, Zeng D. A general framework of nonparametric feature selection in high-dimensional data. Biometrics 2023; 79:951-963. [PMID: 35318639 PMCID: PMC10540052 DOI: 10.1111/biom.13664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 03/14/2022] [Indexed: 02/03/2023]
Abstract
Nonparametric feature selection for high-dimensional data is an important and challenging problem in the fields of statistics and machine learning. Most of the existing methods for feature selection focus on parametric or additive models which may suffer from model misspecification. In this paper, we propose a new framework to perform nonparametric feature selection for both regression and classification problems. Under this framework, we learn prediction functions through empirical risk minimization over a reproducing kernel Hilbert space. The space is generated by a novel tensor product kernel, which depends on a set of parameters that determines the importance of the features. Computationally, we minimize the empirical risk with a penalty to estimate the prediction and kernel parameters simultaneously. The solution can be obtained by iteratively solving convex optimization problems. We study the theoretical property of the kernel feature space and prove the oracle selection property and Fisher consistency of our proposed method. Finally, we demonstrate the superior performance of our approach compared to existing methods via extensive simulation studies and applications to two real studies.
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Affiliation(s)
- Hang Yu
- Department of Statistics and Operation Research, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Yuanjia Wang
- Department of Biostatistics, Columbia University, New York, New York, USA
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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31
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Stickel AM, Tarraf W, González KA, Ivanovic V, Morlett Paredes A, Zeng D, Cai J, Isasi CR, Kaplan R, Lipton RB, Daviglus M, Testai FD, Lamar M, Gallo LC, Talavera GA, Gellman MD, Ramos AR, González HM, DeCarli C. Characterizing age- and sex-related differences in brain structure among middle-aged and older Hispanic/Latino adults in the study of Latinos- investigation of neurocognitive aging magnetic resonance imaging (SOL-INCA MRI). Neurobiol Aging 2023; 126:58-66. [PMID: 36933278 PMCID: PMC10363333 DOI: 10.1016/j.neurobiolaging.2023.02.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 02/15/2023] [Accepted: 02/15/2023] [Indexed: 02/25/2023]
Abstract
Hispanic/Latino adults are a growing segment of the older U.S. population yet are underrepresented in brain aging research. We aimed to characterize brain aging among diverse Hispanic/Latino individuals. Hispanic/Latino individuals (unweighted n = 2273 ages 35-85 years; 56% female) from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) population-based study underwent magnetic resonance imaging (MRI) as part of the SOL- Investigation of Neurocognitive Aging MRI (SOL-INCA-MRI) ancillary study (2018-2022). We performed linear regressions to calculate age associations with brain volumes for each outcome (total (global) brain, hippocampal, lateral ventricle, total white matter hyperintensity (WMH), individual cortical lobar, and total cortical gray matter) and tested modification by sex. Older age was associated with smaller gray matter volumes and larger lateral ventricle and WMH volumes. Age-related differences in global brain volumes and gray matter volumes in specific regions (i.e., the hippocampus and temporal and occipital lobes) were less pronounced among women. Our findings warrant further investigation into sex-specific mechanisms of brain aging using longitudinal studies.
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Affiliation(s)
- Ariana M Stickel
- Department of Neurosciences and Shiley-Marcos Alzheimer's Disease Research Center, University of California, San Diego, La Jolla, CA, USA; Department of Psychology, San Diego State University, San Diego, CA, USA
| | - Wassim Tarraf
- Institute of Gerontology & Department of Healthcare Sciences, Wayne State University, Detroit, MI, USA
| | - Kevin A González
- Department of Neurosciences and Shiley-Marcos Alzheimer's Disease Research Center, University of California, San Diego, La Jolla, CA, USA
| | - Vladamir Ivanovic
- Department of Neurology and Center for Neuroscience, University of California at Davis, Davis, CA, USA
| | - Alejandra Morlett Paredes
- Department of Neurosciences and Shiley-Marcos Alzheimer's Disease Research Center, University of California, San Diego, La Jolla, CA, USA
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
| | - Jianwen Cai
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
| | - Carmen R Isasi
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, The Bronx, NY, USA
| | - Robert Kaplan
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, The Bronx, NY, USA
| | - Richard B Lipton
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, The Bronx, NY, USA; Department of Neurology, Albert Einstein College of Medicine, The Bronx, NY, USA
| | - Martha Daviglus
- Institute for Minority Health Research, University of Illinois at Chicago, College of Medicine, Chicago, IL, USA
| | - Fernando D Testai
- Department of Neurology & Neurorehabilitation, University of Illinois at Chicago, College of Medicine, Chicago, IL, USA
| | - Melissa Lamar
- Institute for Minority Health Research, University of Illinois at Chicago, College of Medicine, Chicago, IL, USA; Rush Alzheimer's Disease Research Center, Rush University Medical Center, Chicago, IL, USA
| | - Linda C Gallo
- Department of Psychology, San Diego State University, San Diego, CA, USA
| | - Gregory A Talavera
- Department of Psychology, San Diego State University, San Diego, CA, USA
| | - Marc D Gellman
- Department of Psychology, University of Miami, Miami, FL, USA
| | - Alberto R Ramos
- Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Hector M González
- Department of Neurosciences and Shiley-Marcos Alzheimer's Disease Research Center, University of California, San Diego, La Jolla, CA, USA
| | - Charles DeCarli
- Department of Neurology and Center for Neuroscience, University of California at Davis, Davis, CA, USA.
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32
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Cai J, Zeng D, Li H, Butera NM, Baldoni PL, Maitra P, Dong L. Comparisons of statistical methods for handling attrition in a follow-up visit with complex survey sampling. Stat Med 2023; 42:1641-1668. [PMID: 37183765 PMCID: PMC10957339 DOI: 10.1002/sim.9692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 02/09/2023] [Accepted: 02/16/2023] [Indexed: 03/09/2023]
Abstract
Design-based analysis, which accounts for the design features of the study, is commonly used to conduct data analysis in studies with complex survey sampling, such as the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). In this type of longitudinal study, attrition has often been a problem. Although there have been various statistical approaches proposed to handle attrition, such as inverse probability weighting (IPW), non-response cell weighting (NRCW), multiple imputation (MI), and full information maximum likelihood (FIML) approach, there has not been a systematic assessment of these methods to compare their performance in design-based analyses. In this article, we perform extensive simulation studies and compare the performance of different missing data methods in linear and generalized linear population models, and under different missing data mechanism. We find that the design-based analysis is able to produce valid estimation and statistical inference when the missing data are handled appropriately using IPW, NRCW, MI, or FIML approach under missing-completely-at-random or missing-at-random missing mechanism and when the missingness model is correctly specified or over-specified. We also illustrate the use of these methods using data from HCHS/SOL.
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Affiliation(s)
- Jianwen Cai
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina
| | - Haolin Li
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina
| | - Nicole M. Butera
- Department of Biostatistics and Bioinformatics, The Biostatistics Center, Milken Institute School of Public Health, The George Washington University, Rockville, Maryland
| | - Pedro L. Baldoni
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina
| | - Poulami Maitra
- Statistics and Data Science, NORC at the University of Chicago, Bethesda, Maryland
| | - Li Dong
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina
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Affiliation(s)
- Dan-Yu Lin
- University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC
| | - Yangjianchen Xu
- University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC
| | - Yu Gu
- University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC
| | - Donglin Zeng
- University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC
| | - Shadia K Sunny
- North Carolina Department of Health and Human Services, Raleigh, NC
| | - Zack Moore
- North Carolina Department of Health and Human Services, Raleigh, NC
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Zou H, Xiao L, Zeng D, Luo S. Multivariate functional mixed model with MRI data: An application to Alzheimer's disease. Stat Med 2023; 42:1492-1511. [PMID: 36805635 PMCID: PMC10133011 DOI: 10.1002/sim.9683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 11/09/2022] [Accepted: 01/26/2023] [Indexed: 02/22/2023]
Abstract
Alzheimer's Disease (AD) is the leading cause of dementia and impairment in various domains. Recent AD studies, (ie, Alzheimer's Disease Neuroimaging Initiative (ADNI) study), collect multimodal data, including longitudinal neurological assessments and magnetic resonance imaging (MRI) data, to better study the disease progression. Adopting early interventions is essential to slow AD progression for subjects with mild cognitive impairment (MCI). It is of particular interest to develop an AD predictive model that leverages multimodal data and provides accurate personalized predictions. In this article, we propose a multivariate functional mixed model with MRI data (MFMM-MRI) that simultaneously models longitudinal neurological assessments, baseline MRI data, and the survival outcome (ie, dementia onset) for subjects with MCI at baseline. Two functional forms (the random-effects model and instantaneous model) linking the longitudinal and survival process are investigated. We use Markov Chain Monte Carlo (MCMC) method based on No-U-Turn Sampling (NUTS) algorithm to obtain posterior samples. We develop a dynamic prediction framework that provides accurate personalized predictions of longitudinal trajectories and survival probability. We apply MFMM-MRI to the ADNI study and identify significant associations among longitudinal outcomes, MRI data, and the risk of dementia onset. The instantaneous model with voxels from the whole brain has the best prediction performance among all candidate models. The simulation study supports the validity of the estimation and dynamic prediction method.
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Affiliation(s)
- Haotian Zou
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, United States
| | - Luo Xiao
- Department of Statistics, North Carolina State University, North Carolina, United States
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina, United States
| | - Sheng Luo
- Department of Biostatistics and Bioinformatics, Duke University, North Carolina, United States
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Duan Z, Li D, Zeng D, Bian Z, Ma J. [A semi-supervised material quantitative intelligent imaging algorithm for spectral CT based on prior information perception learning]. Nan Fang Yi Ke Da Xue Xue Bao 2023; 43:620-630. [PMID: 37202199 DOI: 10.12122/j.issn.1673-4254.2023.04.16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
OBJECTIVE To propose a semi-supervised material quantitative intelligent imaging algorithm based on prior information perception learning (SLMD-Net) to improve the quality and precision of spectral CT imaging. METHODS The algorithm includes a supervised and a self- supervised submodule. In the supervised submodule, the mapping relationship between low and high signal-to-noise ratio (SNR) data was constructed through mean square error loss function learning based on a small labeled dataset. In the self- supervised sub-module, an image recovery model was utilized to construct the loss function incorporating the prior information from a large unlabeled low SNR basic material image dataset, and the total variation (TV) model was used to to characterize the prior information of the images. The two submodules were combined to form the SLMD-Net method, and pre-clinical simulation data were used to validate the feasibility and effectiveness of the algorithm. RESULTS Compared with the traditional model-driven quantitative imaging methods (FBP-DI, PWLS-PCG, and E3DTV), data-driven supervised-learning-based quantitative imaging methods (SUMD-Net and BFCNN), a material quantitative imaging method based on unsupervised learning (UNTV-Net) and semi-supervised learning-based cycle consistent generative adversarial network (Semi-CycleGAN), the proposed SLMD-Net method had better performance in both visual and quantitative assessments. For quantitative imaging of water and bone materials, the SLMD-Net method had the highest PSNR index (31.82 and 29.06), the highest FSIM index (0.95 and 0.90), and the lowest RMSE index (0.03 and 0.02), respectively) and achieved significantly higher image quality scores than the other 7 material decomposition methods (P < 0.05). The material quantitative imaging performance of SLMD-Net was close to that of the supervised network SUMD-Net trained with labeled data with a doubled size. CONCLUSIONS A small labeled dataset and a large unlabeled low SNR material image dataset can be fully used to suppress noise amplification and artifacts in basic material decomposition in spectral CT and reduce the dependence on labeled data-driven network, which considers more realistic scenario in clinics.
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Affiliation(s)
- Z Duan
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
- Guangzhou Key Laboratory of Medical Radioimaging and Detection Technology, Guangzhou 510515, China
| | - D Li
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
- Guangzhou Key Laboratory of Medical Radioimaging and Detection Technology, Guangzhou 510515, China
| | - D Zeng
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
- Guangzhou Key Laboratory of Medical Radioimaging and Detection Technology, Guangzhou 510515, China
| | - Z Bian
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
- Guangzhou Key Laboratory of Medical Radioimaging and Detection Technology, Guangzhou 510515, China
| | - J Ma
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
- Guangzhou Key Laboratory of Medical Radioimaging and Detection Technology, Guangzhou 510515, China
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Lou J, Wang Y, Li L, Zeng D. Estimating individualized treatment rules for multicategory type 2 diabetes treatments using electronic health records. Stat Interface 2023; 16:505-515. [PMID: 38344146 PMCID: PMC10857856 DOI: 10.4310/22-sii739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Abstract
In this article, we propose a general framework to learn optimal treatment rules for type 2 diabetes (T2D) patients using electronic health records (EHRs). We first propose a joint modeling approach to characterize patient's pretreatment conditions using longitudinal markers from EHRs. The estimation accounts for informative measurement times using inverse-intensity weighting methods. The predicted latent processes in the joint model are used to divide patients into a finite of subgroups and, within each group, patients share similar health profiles in EHRs. Within each patient group, we estimate optimal individualized treatment rules by extending a matched learning method to handle multicategory treatments using a one-versus-one approach. Each matched learning for two treatments is implemented by a weighted support vector machine with matched pairs of patients. We apply our method to estimate optimal treatment rules for T2D patients in a large sample of EHRs from the Ohio State University Wexner Medical Center. We demonstrate the utility of our method to select the optimal treatments from four classes of drugs and achieve a better control of glycated hemoglobin than any one-size-fits-all rules.
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Affiliation(s)
- Jitong Lou
- 135 Dauer Drive, 3101 McGavran-Greenberg Hall, Chapel Hill, NC 27599, USA
| | - Yuanjia Wang
- 722 West 168th Street, Rm 210, New York, NY 10032, USA
| | - Lang Li
- 250 Lincoln Tower, 1800 Cannon Drive, Columbus, OH 43210, USA
| | - Donglin Zeng
- 135 Dauer Drive, 3103B McGavran-Greenberg Hall, Chapel Hill, NC 27599, USA
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37
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Jones CW, An X, Ji Y, Liu M, Zeng D, House SL, Beaudoin FL, Stevens JS, Neylan TC, Clifford GD, Jovanovic T, Linnstaedt SD, Germine LT, Bollen KA, Rauch SL, Haran JP, Storrow AB, Lewandowski C, Musey PI, Hendry PL, Sheikh S, Punches BE, Lyons MS, Kurz MC, Swor RA, McGrath ME, Hudak LA, Pascual JL, Seamon MJ, Datner EM, Harris E, Chang AM, Pearson C, Peak DA, Merchant RC, Domeier RM, Rathlev NK, O'Neil BJ, Sergot P, Sanchez LD, Bruce SE, Miller MW, Pietrzak RH, Joormann J, Barch DM, Pizzagalli DA, Sheridan JF, Smoller JW, Harte SE, Elliott JM, Koenen KC, Ressler KJ, Kessler RC, McLean SA. Derivation and Validation of a Brief Emergency Department-Based Prediction Tool for Posttraumatic Stress After Motor Vehicle Collision. Ann Emerg Med 2023; 81:249-261. [PMID: 36328855 DOI: 10.1016/j.annemergmed.2022.08.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 07/28/2022] [Accepted: 08/04/2022] [Indexed: 11/05/2022]
Abstract
STUDY OBJECTIVE To derive and initially validate a brief bedside clinical decision support tool that identifies emergency department (ED) patients at high risk of substantial, persistent posttraumatic stress symptoms after a motor vehicle collision. METHODS Derivation (n=1,282, 19 ED sites) and validation (n=282, 11 separate ED sites) data were obtained from adults prospectively enrolled in the Advancing Understanding of RecOvery afteR traumA study who were discharged from the ED after motor vehicle collision-related trauma. The primary outcome was substantial posttraumatic stress symptoms at 3 months (Posttraumatic Stress Disorder Checklist for Diagnostic and Statistical Manual of Mental Disorders-5 ≥38). Logistic regression derivation models were evaluated for discriminative ability using the area under the curve and the accuracy of predicted risk probabilities (Brier score). Candidate posttraumatic stress predictors assessed in these models (n=265) spanned a range of sociodemographic, baseline health, peritraumatic, and mechanistic domains. The final model selection was based on performance and ease of administration. RESULTS Significant 3-month posttraumatic stress symptoms were common in the derivation (27%) and validation (26%) cohort. The area under the curve and Brier score of the final 8-question tool were 0.82 and 0.14 in the derivation cohort and 0.76 and 0.17 in the validation cohort. CONCLUSION This simple 8-question tool demonstrates promise to risk-stratify individuals with substantial posttraumatic stress symptoms who are discharged to home after a motor vehicle collision. Both external validation of this instrument, and work to further develop more accurate tools, are needed. Such tools might benefit public health by enabling the conduct of preventive intervention trials and assisting the growing number of EDs that provide services to trauma survivors aimed at promoting psychological recovery.
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Affiliation(s)
- Christopher W Jones
- Department of Emergency Medicine, Cooper Medical School of Rowan University, Camden, NJ
| | - Xinming An
- Department of Anesthesiology, Department of Psychiatry, Institute for Trauma Recovery, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Yinyao Ji
- Department of Biostatistics, University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC
| | - Mochuan Liu
- Department of Biostatistics, University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC
| | - Stacey L House
- Department of Emergency Medicine, Washington University School of Medicine, St Louis, MO
| | - Francesca L Beaudoin
- Department of Emergency Medicine and Department of Health Services, Policy, and Practice, The Alpert Medical School of Brown University, Rhode Island Hospital and The Miriam Hospital, Providence, RI
| | - Jennifer S Stevens
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA
| | - Thomas C Neylan
- Department of Psychiatry and Neurology, University of California San Francisco, San Francisco, CA
| | - Gari D Clifford
- Department of Biomedical Informatics, Emory University School of Medicine and Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA
| | - Tanja Jovanovic
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, MI
| | - Sarah D Linnstaedt
- Department of Anesthesiology, Department of Psychiatry, Institute for Trauma Recovery, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Laura T Germine
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA; The Many Brains Project, Belmont, MA; Department of Psychiatry, Harvard Medical School, Boston, MA
| | - Kenneth A Bollen
- Department of Psychology and Neuroscience and Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Scott L Rauch
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA; Department of Psychiatry, Harvard Medical School, Boston, MA; Department of Psychiatry, McLean Hospital, Belmont, MA
| | - John P Haran
- Department of Emergency Medicine, University of Massachusetts Medical School, Worcester, MA
| | - Alan B Storrow
- Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, TN
| | | | - Paul I Musey
- Department of Emergency Medicine, Indiana University School of Medicine, Indianapolis, IN
| | - Phyllis L Hendry
- Department of Emergency Medicine, Indiana University School of Medicine, Indianapolis, IN
| | - Sophia Sheikh
- Department of Emergency Medicine, University of Florida College of Medicine -Jacksonville, Jacksonville, FL; Department of Emergency Medicine, University of Cincinnati College of Medicine, and College of Nursing, University of Cincinnati, Cincinnati, OH
| | - Brittany E Punches
- Department of Emergency Medicine, University of Florida College of Medicine -Jacksonville, Jacksonville, FL
| | - Michael S Lyons
- College of Nursing, University of Cincinnati, Cincinnati, OH
| | - Michael C Kurz
- Department of Emergency Medicine, Division of Acute Care Surgery, Department of Surgery, University of Alabama School of Medicine, and Center for Injury Science, University of Alabama at Birmingham, Birmingham, AL
| | - Robert A Swor
- Department of Emergency Medicine, Oakland University William Beaumont School of Medicine, Rochester, MI
| | - Meghan E McGrath
- Department of Emergency Medicine, Boston Medical Center, Boston, MA
| | - Lauren A Hudak
- Department of Emergency Medicine, Emory University School of Medicine, Atlanta, GA; Department of Surgery, Department of Neurosurgery, University of Pennsylvania, Pennsylvania, PA
| | - Jose L Pascual
- Department of Surgery, Department of Neurosurgery, University of Pennsylvania, Pennsylvania, PA; Perelman School of Medicine, University of Pennsylvania, Pennsylvania, PA
| | - Mark J Seamon
- Division of Traumatology, Department of Surgery, Surgical Critical Care and Emergency Surgery, University of Pennsylvania, Pennsylvania, PA
| | - Elizabeth M Datner
- Department of Emergency Medicine, Einstein Healthcare Network, and the Sidney Kimmel Medical College, Thomas Jefferson University, Pennsylvania, PA
| | | | - Anna M Chang
- Department of Emergency Medicine, Jefferson University Hospitals, Pennsylvania, PA
| | - Claire Pearson
- Department of Emergency Medicine, Wayne State University, Ascension St John Hospital, Detroit, MI
| | - David A Peak
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA
| | - Roland C Merchant
- Department of Emergency Medicine, Brigham and Women's Hospital, Boston, MA
| | - Robert M Domeier
- Department of Emergency Medicine, Saint Joseph Mercy Hospital, Ypsilanti, MI
| | - Niels K Rathlev
- Department of Emergency Medicine, University of Massachusetts Medical School-Baystate, Springfield, MA
| | - Brian J O'Neil
- Department of Emergency Medicine, Wayne State University, Detroit Receiving Hospital, Detroit, MI
| | - Paulina Sergot
- Department of Emergency Medicine, McGovern Medical School, University of Texas Health, Houston, TX
| | - Leon D Sanchez
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA; Department of Emergency Medicine, Harvard Medical School, Boston, MA
| | - Steven E Bruce
- Department of Psychological Sciences, University of Missouri - St Louis, St Louis, MO
| | - Mark W Miller
- National Center for PTSD, Behavioral Science Division, VA Boston Healthcare System, and Department of Psychiatry, Boston University School of Medicine, Boston, MA; Clinical Neurosciences Division, National Center for PTSD, VA Connecticut Healthcare System, West Haven, CT
| | | | - Jutta Joormann
- Department of Psychology, Yale School of Medicine, New Haven, CT
| | - Deanna M Barch
- Department of Psychological and Brain Sciences, Washington University in St Louis, St Louis, MO
| | - Diego A Pizzagalli
- Department of Psychiatry, Harvard Medical School, Boston, MA; Division of Depression and Anxiety, McLean Hospital, Belmont, MA
| | - John F Sheridan
- Department of Biosciences, and the Institute for Behavioral Medicine Research, OSU Wexner Medical Center, Columbus, OH
| | - Jordan W Smoller
- Department of Psychiatry, Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, and Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA
| | - Steven E Harte
- Department of Anesthesiology, and Department of Internal Medicine-Rheumatology, University of Michigan Medical School, Ann Arbor, MI
| | - James M Elliott
- Kolling Institute of Medical Research, University of Sydney, St Leonards, and Faculty of Medicine and Health, University of Sydney, Northern Sydney Local Health District, New South Wales, Australia, and Physical Therapy and Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL
| | - Karestan C Koenen
- Department of Epidemiology, Harvard T H Chan School of Public Health, Harvard University, Boston, MA
| | - Kerry J Ressler
- Department of Psychiatry, Harvard Medical School, Boston, MA; Department of Psychological and Brain Sciences, Washington University in St Louis, St Louis, MO
| | - Ronald C Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, MA
| | - Samuel A McLean
- Departments of Emergency Medicine and Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC.
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Butera NM, Zeng D, Heiss G, Cai J. Modeling longitudinal change in biomarkers using data from a complex survey sampling design: An application to the Hispanic Community Health Study/Study of Latinos. Stat Med 2023; 42:632-655. [PMID: 36631123 PMCID: PMC10936944 DOI: 10.1002/sim.9635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 12/06/2022] [Accepted: 12/12/2022] [Indexed: 01/13/2023]
Abstract
In observational cohort studies, there is frequently interest in modeling longitudinal change in a biomarker (ie, physiological measure indicative of metabolic dysregulation or disease; eg, blood pressure) in the absence of treatment (ie, medication), and its association with modifiable risk factors expected to affect health (eg, body mass index). However, individuals may start treatment during the study period, and consequently biomarker values observed while on treatment may be different than those that would have been observed in the absence of treatment. If treated individuals are excluded from analysis, then effect estimates may be biased if treated individuals differ systematically from untreated individuals. We addressed this concern in the setting of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL), an observational cohort study that employed a complex survey sampling design to enable inference to a finite target population. We considered biomarker values measured while on treatment to be missing data, and applied missing data methodology (inverse probability weighting (IPW) and doubly robust estimation) to this problem. The proposed methods leverage information collected between study visits on when individuals started treatment, by adapting IPW and doubly robust approaches to model the treatment mechanism using survival analysis methods. This methodology also incorporates sampling weights and uses a bootstrap approach to estimate standard errors accounting for the complex survey sampling design. We investigated variance estimation for these methods, conducted simulation studies to assess statistical performance in finite samples, and applied the methodology to model temporal change in blood pressure in HCHS/SOL.
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Affiliation(s)
- Nicole M. Butera
- Department of Biostatistics and Bioinformatics, The Biostatistics Center, Milken Institute School of Public Health, The George Washington University, Rockville, Maryland
| | - Donglin Zeng
- Department of Biostatistics, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Gerardo Heiss
- Department of Epidemiology, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Jianwen Cai
- Department of Biostatistics, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
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39
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Affiliation(s)
- Dan-Yu Lin
- University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC
| | - Yangjianchen Xu
- University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC
| | - Yu Gu
- University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC
| | - Donglin Zeng
- University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC
| | - Bradford Wheeler
- North Carolina Department of Health and Human Services, Raleigh, NC
| | - Hayley Young
- North Carolina Department of Health and Human Services, Raleigh, NC
| | - Shadia K Sunny
- North Carolina Department of Health and Human Services, Raleigh, NC
| | - Zack Moore
- North Carolina Department of Health and Human Services, Raleigh, NC
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40
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Lin T, Peng S, Lu S, Fu S, Zeng D, Li J, Chen T, Fan T, Lang C, Feng S, Ma J, Zhao C, Antony B, Cicuttini F, Quan X, Zhu Z, Ding C. Prediction of knee pain improvement over two years for knee osteoarthritis using a dynamic nomogram based on MRI-derived radiomics: a proof-of-concept study. Osteoarthritis Cartilage 2023; 31:267-278. [PMID: 36334697 DOI: 10.1016/j.joca.2022.10.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 09/26/2022] [Accepted: 10/19/2022] [Indexed: 11/05/2022]
Abstract
OBJECTIVES To develop and validate a nomogram to detect improved knee pain in osteoarthritis (OA) by integrating magnetic resonance imaging (MRI) radiomics signature of subchondral bone and clinical characteristics. METHODS Participants were selected from the Vitamin D Effects on Osteoarthritis (VIDEO) study. The primary outcome was 20% improvement of knee pain score over 2 years in participants administrated either vitamin D or placebo. Radiomics features of subchondral bone and clinical characteristics from 216 participants were extracted and analyzed. The participants were randomly split into the training and validation cohorts at a ratio of 8:2. Least absolute shrinkage and selection operator (LASSO) regression was used to select features and generate radiomics signatures. The optimal radiomics signature and clinical indicators were fitted into a nomogram using multivariable logistic regression model. RESULTS The nomogram showed favorable discrimination performance [AUCtraining, 0.79 (95% CI: 0.72-0.79), AUCvalidation, 0.83 (95% CI: 0.70-0.96)] as well as a good calibration. Additional contributing value of fusion radiomics signature to the nomogram was statistically significant (NRI, 0.23; IDI, 0.14, P < 0.001 in training cohort and NRI, 0.29; IDI, 0.18, P < 0.05 in validating cohort). Decision curve analysis confirmed the clinical usefulness of nomogram. CONCLUSION The radiomics-based nomogram comprising the MR radiomics signature and clinical variables achieves a favorable predictive efficacy and accuracy in differentiating improvement in knee pain among OA patients. This proof-of-concept study provides a promising way to predict clinically meaningful outcomes.
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Affiliation(s)
- T Lin
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
| | - S Peng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
| | - S Lu
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
| | - S Fu
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
| | - D Zeng
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
| | - J Li
- Division of Orthopaedic Surgery, Department of Orthopaedics, Nanfang Hospital, Southern Medical University, Guangzhou, 510282, China.
| | - T Chen
- Clinical Research Centre, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
| | - T Fan
- Clinical Research Centre, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
| | - C Lang
- Clinical Research Centre, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
| | - S Feng
- Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology, 999077, Hong Kong, China.
| | - J Ma
- School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
| | - C Zhao
- Philips China, Beijing, 100000, China.
| | - B Antony
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, 7000, Australia.
| | - F Cicuttini
- Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, 3800, Australia.
| | - X Quan
- Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
| | - Z Zhu
- Clinical Research Centre, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China.
| | - C Ding
- Clinical Research Centre, Zhujiang Hospital, Southern Medical University, Guangzhou, 510282, China; Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, 7000, Australia.
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41
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Beaudoin FL, An X, Basu A, Ji Y, Liu M, Kessler RC, Doughtery RF, Zeng D, Bollen KA, House SL, Stevens JS, Neylan TC, Clifford GD, Jovanovic T, Linnstaedt SD, Germine LT, Rauch SL, Haran JP, Storrow AB, Lewandowski C, Musey PI, Hendry PL, Sheikh S, Jones CW, Punches BE, Kurz MC, Swor RA, Murty VP, McGrath ME, Hudak LA, Pascual JL, Datner EM, Chang AM, Pearson C, Peak DA, Merchant RC, Domeier RM, Rathlev NK, Neil BJO, Sergot P, Sanchez LD, Bruce SE, Baker JT, Joormann J, Miller MW, Pietrzak RH, Barch DM, Pizzagalli DA, Sheridan JF, Smoller JW, Harte SE, Elliott JM, Koenen KC, Ressler KJ, McLean SA. Use of serial smartphone-based assessments to characterize diverse neuropsychiatric symptom trajectories in a large trauma survivor cohort. Transl Psychiatry 2023; 13:4. [PMID: 36609484 PMCID: PMC9823011 DOI: 10.1038/s41398-022-02289-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 10/25/2022] [Accepted: 12/14/2022] [Indexed: 01/09/2023] Open
Abstract
The authors sought to characterize adverse posttraumatic neuropsychiatric sequelae (APNS) symptom trajectories across ten symptom domains (pain, depression, sleep, nightmares, avoidance, re-experiencing, anxiety, hyperarousal, somatic, and mental/fatigue symptoms) in a large, diverse, understudied sample of motor vehicle collision (MVC) survivors. More than two thousand MVC survivors were enrolled in the emergency department (ED) and completed a rotating battery of brief smartphone-based surveys over a 2-month period. Measurement models developed from survey item responses were used in latent growth curve/mixture modeling to characterize homogeneous symptom trajectories. Associations between individual trajectories and pre-trauma and peritraumatic characteristics and traditional outcomes were compared, along with associations within and between trajectories. APNS across all ten symptom domains were common in the first two months after trauma. Many risk factors and associations with high symptom burden trajectories were shared across domains. Both across and within traditional diagnostic boundaries, APNS trajectory intercepts, and slopes were substantially correlated. Across all domains, symptom severity in the immediate aftermath of trauma (trajectory intercepts) had the greatest influence on the outcome. An interactive data visualization tool was developed to allow readers to explore relationships of interest between individual characteristics, symptom trajectories, and traditional outcomes ( http://itr.med.unc.edu/aurora/parcoord/ ). Individuals presenting to the ED after MVC commonly experience a broad constellation of adverse posttraumatic symptoms. Many risk factors for diverse APNS are shared. Individuals diagnosed with a single traditional outcome should be screened for others. The utility of multidimensional categorizations that characterize individuals across traditional diagnostic domains should be explored.
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Affiliation(s)
- Francesca L Beaudoin
- Department of Epidemiology, Brown University, Providence, RI, USA
- Department of Emergency Medicine, Brown University, Providence, RI, USA
| | - Xinming An
- Institute for Trauma Recovery, Department of Anesthesiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Archana Basu
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Yinyao Ji
- Institute for Trauma Recovery, Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Mochuan Liu
- Institute for Trauma Recovery, Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Ronald C Kessler
- Department of Health Care Policy, Harvard Medical School, Boston, MA, USA
| | | | - Donglin Zeng
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Kenneth A Bollen
- Department of Psychology and Neuroscience & Department of Sociology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Stacey L House
- Department of Emergency Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Jennifer S Stevens
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Thomas C Neylan
- Departments of Psychiatry and Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Gari D Clifford
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA
| | - Tanja Jovanovic
- Department of Psychiatry and Behavioral Neurosciences, Wayne State University, Detroit, MA, USA
| | - Sarah D Linnstaedt
- Institute for Trauma Recovery, Department of Anesthesiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Laura T Germine
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA
- The Many Brains Project, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
| | - Scott L Rauch
- Institute for Technology in Psychiatry, McLean Hospital, Belmont, MA, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, McLean Hospital, Belmont, MA, USA
| | - John P Haran
- Department of Emergency Medicine, University of Massachusetts Medical School, Worcester, MA, USA
| | - Alan B Storrow
- Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Paul I Musey
- Department of Emergency Medicine, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Phyllis L Hendry
- Department of Emergency Medicine, University of Florida College of Medicine -Jacksonville, Jacksonville, FL, USA
| | - Sophia Sheikh
- Department of Emergency Medicine, University of Florida College of Medicine -Jacksonville, Jacksonville, FL, USA
| | - Christopher W Jones
- Department of Emergency Medicine, Cooper Medical School of Rowan University, Camden, NJ, USA
| | - Brittany E Punches
- Department of Emergency Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- College of Nursing, University of Cincinnati, Cincinnati, OH, USA
| | - Michael C Kurz
- Department of Emergency Medicine, University of Alabama School of Medicine, Birmingham, AL, USA
- Department of Surgery, Division of Acute Care Surgery, University of Alabama School of Medicine, Birmingham, AL, USA
- Center for Injury Science, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Robert A Swor
- Department of Emergency Medicine, Oakland University William Beaumont School of Medicine, Rochester, MI, USA
| | - Vishnu P Murty
- Department of Psychology, Temple University, Philadelphia, PA, USA
| | - Meghan E McGrath
- Department of Emergency Medicine, Boston Medical Center, Boston, MA, USA
| | - Lauren A Hudak
- Department of Emergency Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Jose L Pascual
- Department of Surgery, Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth M Datner
- Department of Emergency Medicine, Einstein Healthcare Network, Philadelphia, PA, USA
- Department of Emergency Medicine, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
| | - Anna M Chang
- Department of Emergency Medicine, Jefferson University Hospitals, Philadelphia, PA, USA
| | - Claire Pearson
- Department of Emergency Medicine, Wayne State University, Detroit, MI, USA
| | - David A Peak
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Roland C Merchant
- Department of Emergency Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Robert M Domeier
- Department of Emergency Medicine, Saint Joseph Mercy Hospital, Ypsilanti, MI, USA
| | - Niels K Rathlev
- Department of Emergency Medicine, University of Massachusetts Medical School-Baystate, Springfield, MA, USA
| | - Brian J O' Neil
- Department of Emergency Medicine, Wayne State University, Detroit, MI, USA
| | - Paulina Sergot
- Department of Emergency Medicine, McGovern Medical School, University of Texas Health, Houston, TX, USA
| | - Leon D Sanchez
- Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Emergency Medicine, Harvard Medical School, Boston, MA, USA
| | - Steven E Bruce
- Department of Psychological Sciences, University of Missouri - St. Louis, St. Louis, MO, USA
| | | | - Jutta Joormann
- Department of Psychology, Yale University, West Haven, CT, USA
| | - Mark W Miller
- National Center for PTSD, Behavioral Science Division, VA Boston Healthcare System, Boston, MA, USA
- Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
| | - Robert H Pietrzak
- National Center for PTSD, Clinical Neurosciences Division, VA Connecticut Healthcare System, West Haven, CT, USA
- Department of Psychiatry, Yale School of Medicine, West Haven, CT, USA
| | - Deanna M Barch
- Department of Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO, USA
| | - Diego A Pizzagalli
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA
| | - John F Sheridan
- Department of Biosciences, OSU Wexner Medical Center, Columbus, OH, USA
- Institute for Behavioral Medicine Research, OSU Wexner Medical Center, Columbus, OH, USA
| | - Jordan W Smoller
- Department of Psychiatry, Psychiatric and Neurodevelopmental Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA
| | - Steven E Harte
- Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, USA
- Department of Internal Medicine-Rheumatology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - James M Elliott
- Kolling Institute of Medical Research, University of Sydney, St Leonards, NSW, Australia
- Faculty of Medicine and Health, University of Sydney, Northern Sydney Local, Health District, NSW, Australia
- Physical Therapy & Human Movement Sciences, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Karestan C Koenen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Kerry J Ressler
- Department of Psychiatry, Harvard Medical School, Boston, MA, USA
- Division of Depression and Anxiety, McLean Hospital, Belmont, MA, USA
| | - Samuel A McLean
- Institute for Trauma Recovery, Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Emergency Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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42
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Wong KY, Zeng D, Lin D. Penalized Regression for Multiple Types of Many Features With Missing Data. Stat Sin 2023; 33:633-662. [PMID: 37197479 PMCID: PMC10187615 DOI: 10.5705/ss.202020.0401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Recent technological advances have made it possible to measure multiple types of many features in biomedical studies. However, some data types or features may not be measured for all study subjects because of cost or other constraints. We use a latent variable model to characterize the relationships across and within data types and to infer missing values from observed data. We develop a penalized-likelihood approach for variable selection and parameter estimation and devise an efficient expectation-maximization algorithm to implement our approach. We establish the asymptotic properties of the proposed estimators when the number of features increases at a polynomial rate of the sample size. Finally, we demonstrate the usefulness of the proposed methods using extensive simulation studies and provide an application to a motivating multi-platform genomics study.
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43
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Zhang Y, Elgart M, Granot-Hershkovitz E, Wang H, Tarraf W, Ramos AR, Stickel AM, Zeng D, Garcia TP, Testai FD, Wassertheil-Smoller S, Isasi CR, Daviglus ML, Kaplan R, Fornage M, DeCarli C, Redline S, González HM, Sofer T. Genetic associations between sleep traits and cognitive ageing outcomes in the Hispanic Community Health Study/Study of Latinos. EBioMedicine 2023; 87:104393. [PMID: 36493726 PMCID: PMC9732133 DOI: 10.1016/j.ebiom.2022.104393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 11/18/2022] [Accepted: 11/18/2022] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Sleep phenotypes have been reported to be associated with cognitive ageing outcomes. However, there is limited research using genetic variants as proxies for sleep traits to study their associations. We estimated associations between Polygenic Risk Scores (PRSs) for sleep duration, insomnia, daytime sleepiness, and obstructive sleep apnoea (OSA) and measures of cogntive ageing in Hispanic/Latino adults. METHODS We used summary statistics from published genome-wide association studies to construct PRSs representing the genetic basis of each sleep trait, then we studied the association of the PRSs of the sleep phenotypes with cognitive outcomes in the Hispanic Community Healthy Study/Study of Latinos. The primary model adjusted for age, sex, study centre, and measures of genetic ancestry. Associations are highlighted if their p-value <0.05. FINDINGS Higher PRS for insomnia was associated with lower global cognitive function and higher risk of mild cognitive impairment (MCI) (OR = 1.20, 95% CI [1.06, 1.36]). Higher PRS for daytime sleepiness was also associated with increased MCI risk (OR = 1.14, 95% CI [1.02, 1.28]). Sleep duration PRS was associated with reduced MCI risk among short and normal sleepers, while among long sleepers it was associated with reduced global cognitive function and with increased MCI risk (OR = 1.40, 95% CI [1.10, 1.78]). Furthermore, adjustment of analyses for the measured sleep phenotypes and APOE-ε4 allele had minor effects on the PRS associations with the cognitive outcomes. INTERPRETATION Genetic measures underlying insomnia, daytime sleepiness, and sleep duration are associated with MCI risk. Genetic and self-reported sleep duration interact in their effect on MCI. FUNDING Described in Acknowledgments.
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Affiliation(s)
- Yuan Zhang
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Department of Respiratory Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Michael Elgart
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Einat Granot-Hershkovitz
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Heming Wang
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Wassim Tarraf
- Institute of Gerontology, Wayne State University, Detroit, MI, USA
| | - Alberto R Ramos
- Department of Neurology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Ariana M Stickel
- Department of Psychology, San Diego State University, San Diego, CA, USA
| | - Donglin Zeng
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Tanya P Garcia
- Department of Biostatistics, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, NC, USA
| | - Fernando D Testai
- Department of Neurology and Rehabilitation, University of Illinois College of Medicine at Chicago, Chicago, IL, USA
| | | | - Carmen R Isasi
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Martha L Daviglus
- Institute for Minority Health Research, University of Illinois at Chicago, Chicago, IL, USA
| | - Robert Kaplan
- Department of Epidemiology & Population Health, Albert Einstein College of Medicine, Bronx, NY, USA; Public Health Sciences Division, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Charles DeCarli
- Department of Neurology, Alzheimer's Disease Center, University of California, Davis, Sacramento, CA, USA
| | - Susan Redline
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Hector M González
- Department of Neurosciences and Shiley-Marcos Alzheimer's Disease Center, University of California, San Diego, La Jolla, CA, USA
| | - Tamar Sofer
- Division of Sleep and Circadian Disorders, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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Ma H, Zeng D, Liu Y. Learning Optimal Group-structured Individualized Treatment Rules with Many Treatments. J Mach Learn Res 2023; 24:102. [PMID: 37588020 PMCID: PMC10426767] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/18/2023]
Abstract
Data driven individualized decision making problems have received a lot of attentions in recent years. In particular, decision makers aim to determine the optimal Individualized Treatment Rule (ITR) so that the expected specified outcome averaging over heterogeneous patient-specific characteristics is maximized. Many existing methods deal with binary or a moderate number of treatment arms and may not take potential treatment effect structure into account. However, the effectiveness of these methods may deteriorate when the number of treatment arms becomes large. In this article, we propose GRoup Outcome Weighted Learning (GROWL) to estimate the latent structure in the treatment space and the optimal group-structured ITRs through a single optimization. In particular, for estimating group-structured ITRs, we utilize the Reinforced Angle based Multicategory Support Vector Machines (RAMSVM) to learn group-based decision rules under the weighted angle based multi-class classification framework. Fisher consistency, the excess risk bound, and the convergence rate of the value function are established to provide a theoretical guarantee for GROWL. Extensive empirical results in simulation studies and real data analysis demonstrate that GROWL enjoys better performance than several other existing methods.
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Affiliation(s)
- Haixu Ma
- Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yufeng Liu
- Department of Statistics and Operations Research, Department of Genetics, Department of Biostatistics, Carolina Center for Genome Science, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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45
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Gao F, Zeng D, Wang Y. Semiparametric regression analysis of bivariate censored events in a family study of Alzheimer's disease. Biostatistics 2022; 24:32-51. [PMID: 33948627 DOI: 10.1093/biostatistics/kxab014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 03/21/2021] [Accepted: 03/25/2021] [Indexed: 12/16/2022] Open
Abstract
Assessing disease comorbidity patterns in families represents the first step in gene mapping for diseases and is central to the practice of precision medicine. One way to evaluate the relative contributions of genetic risk factor and environmental determinants of a complex trait (e.g., Alzheimer's disease [AD]) and its comorbidities (e.g., cardiovascular diseases [CVD]) is through familial studies, where an initial cohort of subjects are recruited, genotyped for specific loci, and interviewed to provide extensive disease history in family members. Because of the retrospective nature of obtaining disease phenotypes in family members, the exact time of disease onset may not be available such that current status data or interval-censored data are observed. All existing methods for analyzing these family study data assume single event subject to right-censoring so are not applicable. In this article, we propose a semiparametric regression model for the family history data that assumes a family-specific random effect and individual random effects to account for the dependence due to shared environmental exposures and unobserved genetic relatedness, respectively. To incorporate multiple events, we jointly model the onset of the primary disease of interest and a secondary disease outcome that is subject to interval-censoring. We propose nonparametric maximum likelihood estimation and develop a stable Expectation-Maximization (EM) algorithm for computation. We establish the asymptotic properties of the resulting estimators and examine the performance of the proposed methods through simulation studies. Our application to a real world study reveals that the main contribution of comorbidity between AD and CVD is due to genetic factors instead of environmental factors.
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Affiliation(s)
- Fei Gao
- Division of Vaccine and Infectious Disease, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Yuanjia Wang
- Department of Biostatistics, Columbia University, New York, NY 10032, USA
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Stickel AM, Tarraf W, Wu B, Sundermann EE, Gallo LC, Lamar M, Daviglus ML, Zeng D, Thyagarajan B, Isasi CR, Lipton RB, Cordero C, Perreira KM, González HM, Banks SJ. Connections between reproductive health and cognitive aging among women enrolled in the HCHS/SOL and SOL‐INCA. Alzheimers Dement 2022. [DOI: 10.1002/alz.064686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
| | | | - Benson Wu
- University of California, San Diego La Jolla CA USA
| | | | | | - Melissa Lamar
- Rush Alzheimer’s Disease Center Chicago IL USA
- University of Illinois at Chicago, College of Medicine Chicago IL USA
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Paredes AM, Tarraf W, Gonzalez KA, Stickel AM, Graves LV, Salmon DP, Kaur S, Gallo LC, Isasi CR, Lipton RB, Lamar M, Goodman ZT, Zeng D, Garcia TP, González HM. Normative data for the Digit Symbol Substitution Test for diverse Hispanic/Latino adults: Results from the Study of Latinos‐Investigation of Neurocognitive Aging (SOL‐INCA). Alzheimers Dement 2022. [DOI: 10.1002/alz.066604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
| | | | | | | | - Lisa V. Graves
- California State University San Marcos San Marcos CA USA
| | - David P. Salmon
- University of California San Diego La Jolla CA USA
- Shiley‐Marcos Alzheimer’s Disease Research Center La Jolla CA USA
| | - Sonya Kaur
- University of Miami Miller School of Medicine Miami FL USA
| | | | | | | | - Melissa Lamar
- Rush Alzheimer’s Disease Center Chicago IL USA
- University of Illinois at Chicago, College of Medicine Chicago IL USA
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Lin Y, Tang M, Liu Y, Jiang M, He S, Zeng D, Cui MY. A narrative review on machine learning in diagnosis and prognosis prediction for tongue squamous cell carcinoma. Transl Cancer Res 2022; 11:4409-4415. [PMID: 36644177 PMCID: PMC9834582 DOI: 10.21037/tcr-22-1669] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 11/07/2022] [Indexed: 12/28/2022]
Abstract
Background Tongue squamous cell carcinoma (TSCC) is the most common subtype of oral cavity squamous cell carcinoma (OCSCC), and it also has the worst prognosis. It is crucial to find an effective way to solve the challenges in diagnosis and prognosis prediction for TSCC. Machine learning (ML) has been widely used in medical research and has shown good performance. It can be used for feature extraction, feature selection, model construction, etc. Radiomics and deep learning (DL), the new components of ML, have also been utilized to explore the relationship between image features and diseases. The current study aimed to highlight the importance of ML as a potential method for addressing the challenges in diagnosis and prognosis prediction of TSCC by reviewing studies on ML in TSCC. Methods The studies on ML in TSCC in PubMed, Scopus, Web of Science, and China National Knowledge Infrastructure published between the dates of inception of these databases and April 30, 2022, were reviewed. Key Content and Findings ML (including radiomics and DL) which was used in diagnosis and prognosis prediction for TSCC, has shown promising performance. Conclusions Despite its limitations, ML is still a potential approach that can help to deal with the challenges in diagnosis and prognosis prediction for TSCC. Nevertheless, more efforts are needed to enhance the usefulness of ML in this field.
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Affiliation(s)
- Yingyu Lin
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Mimi Tang
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yi Liu
- Department of Radiology, Hospital of Stomatology, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Mengjie Jiang
- Department of Radiology, Hospital of Stomatology, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Shuangshuang He
- Department of Radiology, Hospital of Stomatology, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Donglin Zeng
- Department of Radiology, Hospital of Stomatology, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
| | - Min-Yi Cui
- Department of Radiology, Hospital of Stomatology, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Stomatology, Guangzhou, China
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49
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Tarraf W, Stickel AM, Wu B, Brewer JB, Gallo LC, Talavera GA, Isasi CR, Kaplan R, Lipton RB, Daviglus ML, Zeng D, Pike JR, Schneiderman N, Rundek T, Sofer T, Fornage M, DeCarli CS, Fletcher E, Branch C, Zhou X, Gonzalez HL. Cardiovascular health and resilience to cognitive decline and impairment among diverse Hispanics/Latinos: Results from the Study of Latinos‐ Investigation of Neurocognitive Aging (SOL‐INCA). Alzheimers Dement 2022. [DOI: 10.1002/alz.064687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
| | | | - Benson Wu
- University of California, San Diego La Jolla CA USA
| | | | | | | | | | | | | | | | | | | | | | - Tatjana Rundek
- University of Miami Miller School of Medicine Miami FL USA
| | | | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School; School of Public Health, The University of Texas Health Science Center Houston TX USA
| | | | | | - Craig Branch
- Albert Einstein College of Medicine Bronx NY USA
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50
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Mendoza A, Stickel AM, Tarraf W, Kuwayama S, Kaur S, Paredes AM, Daviglus ML, Testai FD, Zeng D, Isasi CR, Baiduc RR, Dinces E, Lee DJ, González HM. Relationships between hearing impairment with 7‐year cognition and cognitive change: Results from the Study of Latinos‐ Investigation of Neurocognitive Aging (SOL‐INCA). Alzheimers Dement 2022. [DOI: 10.1002/alz.064685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
| | | | | | | | - Sonya Kaur
- University of Miami Miller School of Medicine Miami FL USA
| | | | | | - Fenando D Testai
- University of Illinois at Chicago, College of Medicine Chicago IL USA
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