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Kobara S, Yamamoto R, Rad MG, Grunwell JR, Hikota N, Uzawa Y, Hayashi Y, Coopersmith CM, Kamaleswaran R. Association between comorbidities at ICU admission and post-Sepsis physical impairment: A retrospective cohort study. J Crit Care 2024; 83:154833. [PMID: 38776846 DOI: 10.1016/j.jcrc.2024.154833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 04/27/2024] [Accepted: 05/13/2024] [Indexed: 05/25/2024]
Abstract
PURPOSE Few studies have measured the association between pre-existing comorbidities and post-sepsis physical impairment. The study aimed to estimate the risk of physical impairment at hospital discharge among sepsis patients, adjusting for pre-existing physical impairment prior to ICU admission and in-hospital mortality. MATERIALS AND METHODS We analyzed all consecutive adult patients admitted to an ICU in a tertiary community hospital, Kameda Medical Center, with sepsis diagnosis from September 2014 to October 2020. Inverse probability attrition weighting using machine learning was employed to estimate the risk of physical impairment at hospital discharge for sepsis patients with and without pre-existing comorbidities at ICU admission. This estimation was adjusted for baseline covariates, pre-ICU physical impairment, and in-hospital mortality. RESULTS Of 889 sepsis patients analyzed, 668 [75.1%] had at least one comorbidity and 221 [24.9%] had no comorbidities at ICU admission. Upon adjusting for baseline covariates, pre-ICU physical impairment, and in-hospital mortality, pre-existing comorbidities were not associated with an elevated risk of physical impairment at hospital discharge (RR: 1.02, 95% CI: 0.92, 1.14). CONCLUSIONS Pre-existing comorbidities prior to ICU admission were not associated with an increased risk of physical impairment at hospital discharge among sepsis patients after adjusting for baseline covariates and in-hospital mortality.
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Affiliation(s)
- Seibi Kobara
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA.
| | - Ryohei Yamamoto
- Department of Healthcare Epidemiology, School of Public Health in the Graduate School of Medicine, Kyoto University, Kyoto, Japan; Department of Intensive Care, Kameda Medical Center, Chiba, Japan
| | - Milad G Rad
- Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Jocelyn R Grunwell
- Department of Pediatrics, Division of Critical Care, Emory University School of Medicine, Atlanta, GA, USA; Children's Healthcare of Atlanta, GA, USA
| | - Nao Hikota
- Department of Rehabilitation, Kameda Medical Center, Chiba, Japan
| | - Yoshihiro Uzawa
- Department of Rehabilitation, Kameda Medical Center, Chiba, Japan
| | - Yoshiro Hayashi
- Department of Intensive Care, Kameda Medical Center, Chiba, Japan
| | - Craig M Coopersmith
- Department of Surgery, Emory University School of Medicine, Atlanta, GA, USA; Emory Critical Care Center, Emory University School of Medicine, Atlanta, GA, USA
| | - Rishikesan Kamaleswaran
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA; Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA; Department of Pediatrics, Division of Critical Care, Emory University School of Medicine, Atlanta, GA, USA; Emory Critical Care Center, Emory University School of Medicine, Atlanta, GA, USA; Department of Emergency Medicine, Emory University School of Medicine, Atlanta, GA, USA
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Bohr NL, Brown G, Rakel B, Babrowski T, Dorsey C, Skelly C. Predictive Modeling for One-Year Lower Extremity Endovascular Revascularization Failure in Black Persons. J Surg Res 2024; 300:117-126. [PMID: 38805844 DOI: 10.1016/j.jss.2024.04.068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 04/04/2024] [Accepted: 04/30/2024] [Indexed: 05/30/2024]
Abstract
INTRODUCTION Black persons bear a disproportionate burden of peripheral artery disease (PAD) and experience higher rates of endovascular revascularization failure (ERF) when compared with non-Hispanic White persons. We aimed to identify predictors of ERF in Black persons using predictive modeling. METHODS This retrospective study included all persons identifying as Black who underwent an initial endovascular revascularization procedure for PAD between 2011 and 2018 at a midwestern tertiary care center. Three predictive models were developed using (1) logistic regression, (2) penalized logistic regression (least absolute shrinkage and selection operator [LASSO]), and (3) random forest (RF). Predictive performance was evaluated under repeated cross-validation. RESULTS Of the 163 individuals included in the study, 113 (63.1%) experienced ERF at 1 y. Those with ERF had significant differences in symptom status (P < 0.001), lesion location (P < 0.001), diabetes status (P = 0.037), and annual procedural volume of the attending surgeon (P < 0.001). Logistic regression and LASSO models identified tissue loss, smoking, femoro-popliteal lesion location, and diabetes control as risk factors for ERF. The RF model identified annual procedural volume, age, PAD symptoms, number of comorbidities, and lesion location as most predictive variables. LASSO and RF models were more sensitive than logistic regression but less specific, although all three methods had an overall accuracy of ≥75%. CONCLUSIONS Black persons undergoing endovascular revascularization for PAD are at high risk of ERF, necessitating need for targeted intervention. Predictive models may be clinically useful for identifying high-risk patients, although individual predictors of ERF varied by model. Further exploration into these models may improve limb salvage for this population.
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Affiliation(s)
- Nicole L Bohr
- Department of Nursing Research, UChicago Medicine, Chicago, Illinois; Department of Surgery, Section of Vascular and Endovascular Surgery, University of Chicago, Chicago, Illinois.
| | - Grant Brown
- Department of Biostatistics, University of Iowa, Iowa City, Iowa
| | - Barbara Rakel
- College of Nursing, University of Iowa, Iowa City, Iowa
| | - Trissa Babrowski
- Department of Surgery, Section of Vascular and Endovascular Surgery, University of Chicago, Chicago, Illinois
| | - Chelsea Dorsey
- Department of Surgery, Section of Vascular and Endovascular Surgery, University of Chicago, Chicago, Illinois
| | - Christopher Skelly
- Department of Surgery, Section of Vascular and Endovascular Surgery, University of Chicago, Chicago, Illinois
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Kosko M, Wang L, Santacatterina M. A fast bootstrap algorithm for causal inference with large data. Stat Med 2024; 43:2894-2927. [PMID: 38738397 DOI: 10.1002/sim.10075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 11/18/2023] [Accepted: 03/22/2024] [Indexed: 05/14/2024]
Abstract
Estimating causal effects from large experimental and observational data has become increasingly prevalent in both industry and research. The bootstrap is an intuitive and powerful technique used to construct standard errors and confidence intervals of estimators. Its application however can be prohibitively demanding in settings involving large data. In addition, modern causal inference estimators based on machine learning and optimization techniques exacerbate the computational burden of the bootstrap. The bag of little bootstraps has been proposed in non-causal settings for large data but has not yet been applied to evaluate the properties of estimators of causal effects. In this article, we introduce a new bootstrap algorithm called causal bag of little bootstraps for causal inference with large data. The new algorithm significantly improves the computational efficiency of the traditional bootstrap while providing consistent estimates and desirable confidence interval coverage. We describe its properties, provide practical considerations, and evaluate the performance of the proposed algorithm in terms of bias, coverage of the true 95% confidence intervals, and computational time in a simulation study. We apply it in the evaluation of the effect of hormone therapy on the average time to coronary heart disease using a large observational data set from the Women's Health Initiative.
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Affiliation(s)
- Matthew Kosko
- Department of Statistics, George Washington University, Washington, DC
| | - Lin Wang
- Department of Statistics, Purdue University, West Lafayette, Indiana
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4
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Gutman R, Karavani E, Shimoni Y. Improving Inverse Probability Weighting by Post-calibrating Its Propensity Scores. Epidemiology 2024; 35:473-480. [PMID: 38619218 PMCID: PMC11191550 DOI: 10.1097/ede.0000000000001733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 03/18/2024] [Indexed: 04/16/2024]
Abstract
Theoretical guarantees for causal inference using propensity scores are partially based on the scores behaving like conditional probabilities. However, prediction scores between zero and one do not necessarily behave like probabilities, especially when output by flexible statistical estimators. We perform a simulation study to assess the error in estimating the average treatment effect before and after applying a simple and well-established postprocessing method to calibrate the propensity scores. We observe that postcalibration reduces the error in effect estimation and that larger improvements in calibration result in larger improvements in effect estimation. Specifically, we find that expressive tree-based estimators, which are often less calibrated than logistic regression-based models initially, tend to show larger improvements relative to logistic regression-based models. Given the improvement in effect estimation and that postcalibration is computationally cheap, we recommend its adoption when modeling propensity scores with expressive models.
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Affiliation(s)
- Rom Gutman
- From the IBM Research, University of Haifa Campus
- Technion - Israel Institute of Technology, Haifa, Israel
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Polick CS, Dennis P, Calhoun PS, Braley TJ, Lee E, Wilson S. Investigating disparities in smoking cessation treatment for veterans with multiple sclerosis: A national analysis. Brain Behav 2024; 14:e3513. [PMID: 38698620 PMCID: PMC11066415 DOI: 10.1002/brb3.3513] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 04/02/2024] [Accepted: 04/13/2024] [Indexed: 05/05/2024] Open
Abstract
BACKGROUND AND AIMS Smoking is a risk factor for multiple sclerosis (MS) development, symptom burden, decreased medication efficacy, and increased disease-related mortality. Veterans with MS (VwMS) smoke at critically high rates; however, treatment rates and possible disparities are unknown. To promote equitable treatment, we aim to investigate smoking cessation prescription practices for VwMS across social determinant factors. METHODS We extracted data from the national Veterans Health Administration electronic health records between October 1, 2017, and September 30, 2018. To derive marginal estimates of the association of MS with receipt of smoking-cessation pharmacotherapy, we used propensity score matching through the extreme gradient boosting machine learning model. VwMS who smoke were matched with veterans without MS who smoke on factors including age, race, depression, and healthcare visits. To assess the marginal association of MS with different cessation treatments, we used logistic regression and conducted stratified analyses by sex, race, and ethnicity. RESULTS The matched sample achieved a good balance across most covariates, compared to the pre-match sample. VwMS (n = 3320) had decreased odds of receiving prescriptions for nicotine patches ([Odds Ratio]OR = 0.86, p < .01), non-patch nicotine replacement therapy (NRT; OR = 0.81, p < .001), and standard practice dual NRT (OR = 0.77, p < .01), compared to matches without MS (n = 13,280). Men with MS had lower odds of receiving prescriptions for nicotine patches (OR = 0.88, p = .05), non-patch NRT (OR = 0.77, p < .001), and dual NRT (OR = 0.72, p < .001). Similarly, Black VwMS had lower odds of receiving prescriptions for patches (OR = 0.62, p < .001), non-patch NRT (OR = 0.75, p < .05), and dual NRT (OR = 0.52, p < .01). The odds of receiving prescriptions for bupropion or varenicline did not differ between VwMS and matches without MS. CONCLUSION VwMS received significantly less smoking cessation treatment, compared to matched controls without MS, showing a critical gap in health services as VwMS are not receiving dual NRT as the standard of care. Prescription rates were especially lower for male and Black VwMS, suggesting that under-represented demographic groups outside of the white female category, most often considered as the "traditional MS" group, could be under-treated regarding smoking cessation support. This foundational work will help inform future work to promote equitable treatment and implementation of cessation interventions for people living with MS.
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Affiliation(s)
- Carri S. Polick
- Durham VA Health Care SystemDurhamNorth CarolinaUSA
- School of Nursing, Duke UniversityDurhamNorth CarolinaUSA
| | - Paul Dennis
- Durham VA Health Care SystemDurhamNorth CarolinaUSA
- Department of Psychiatry and Behavioral SciencesDuke University School of MedicineDurhamNorth CarolinaUSA
| | - Patrick S. Calhoun
- Durham VA Health Care SystemDurhamNorth CarolinaUSA
- Department of Psychiatry and Behavioral SciencesDuke University School of MedicineDurhamNorth CarolinaUSA
| | | | | | - Sarah Wilson
- Durham VA Health Care SystemDurhamNorth CarolinaUSA
- Department of Psychiatry and Behavioral SciencesDuke University School of MedicineDurhamNorth CarolinaUSA
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Tang Y, Wang CH, Mitra P, Pai TW. Noninvasive Risk Prediction Models for Heart Failure Using Proportional Jaccard Indices and Comorbidity Patterns. Rev Cardiovasc Med 2024; 25:179. [PMID: 39076472 PMCID: PMC11267177 DOI: 10.31083/j.rcm2505179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 03/04/2024] [Accepted: 03/12/2024] [Indexed: 07/31/2024] Open
Abstract
Background In the post-coronavirus disease 2019 (COVID-19) era, remote diagnosis and precision preventive medicine have emerged as pivotal clinical medicine applications. This study aims to develop a digital health-monitoring tool that utilizes electronic medical records (EMRs) as the foundation for performing a non-random correlation analysis among different comorbidity patterns for heart failure (HF). Methods Novel similarity indices, including proportional Jaccard index (PJI), multiplication of the odds ratio proportional Jaccard index (OPJI), and alpha proportional Jaccard index (APJI), provide a fundamental framework for constructing machine learning models to predict the risk conditions associated with HF. Results Our models were constructed for different age groups and sexes and yielded accurate predictions of high-risk HF across demographics. The results indicated that the optimal prediction model achieved a notable accuracy of 82.1% and an area under the curve (AUC) of 0.878. Conclusions Our noninvasive HF risk prediction system is based on historical EMRs and provides a practical approach. The proposed indices provided simple and straightforward comparative indicators of comorbidity pattern matching within individual EMRs. All source codes developed for our noninvasive prediction models can be retrieved from GitHub.
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Affiliation(s)
- Yueh Tang
- Department of Computer Science and Information Engineering, National Taipei University of Technology, 106344 Taipei, Taiwan
| | - Chao-Hung Wang
- Heart Failure Research Center, Division of Cardiology, Department of Internal Medicine, Chang Gung Memorial Hospital, 204201 Keelung, Taiwan
- College of Medicine, Chang Gung University, 333323 Taoyua, Taiwan
| | - Prasenjit Mitra
- L3S Research Center, 30167 Hannover, Germany
- College of Information Sciences and Technology, The Pennsylvania State University, University Park, PA 16802, USA
| | - Tun-Wen Pai
- Department of Computer Science and Information Engineering, National Taipei University of Technology, 106344 Taipei, Taiwan
- Department of Computer Science and Engineering, National Taiwan Ocean University, 202301 Keelun, Taiwan
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Andargoli AE, Ulapane N, Nguyen TA, Shuakat N, Zelcer J, Wickramasinghe N. Intelligent decision support systems for dementia care: A scoping review. Artif Intell Med 2024; 150:102815. [PMID: 38553156 DOI: 10.1016/j.artmed.2024.102815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 12/11/2023] [Accepted: 02/12/2024] [Indexed: 04/02/2024]
Abstract
In the context of dementia care, Artificial Intelligence (AI) powered clinical decision support systems have the potential to enhance diagnosis and management. However, the scope and challenges of applying these technologies remain unclear. This scoping review aims to investigate the current state of AI applications in the development of intelligent decision support systems for dementia care. We conducted a comprehensive scoping review of empirical studies that utilised AI-powered clinical decision support systems in dementia care. The results indicate that AI applications in dementia care primarily focus on diagnosis, with limited attention to other aspects outlined in the World Health Organization (WHO) Global Action Plan on the Public Health Response to Dementia 2017-2025 (GAPD). A trifecta of challenges, encompassing data availability, cost considerations, and AI algorithm performance, emerges as noteworthy barriers in adoption of AI applications in dementia care. To address these challenges and enhance AI reliability, we propose a novel approach: a digital twin-based patient journey model. Future research should address identified gaps in GAPD action areas, navigate data-related obstacles, and explore the implementation of digital twins. Additionally, it is imperative to emphasize that addressing trust and combating the stigma associated with AI in healthcare should be a central focus of future research directions.
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Affiliation(s)
| | | | - Tuan Anh Nguyen
- Swinburne University of Technology, Melbourne, Australia; National Ageing Research Institute, Australia
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Ebrahimi R, Dennis PA, Alvarez CA, Shroyer AL, Beckham JC, Sumner JA. Posttraumatic Stress Disorder Is Associated With Elevated Risk of Incident Stroke and Transient Ischemic Attack in Women Veterans. J Am Heart Assoc 2024; 13:e033032. [PMID: 38410963 PMCID: PMC10944021 DOI: 10.1161/jaha.123.033032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2023] [Accepted: 02/06/2024] [Indexed: 02/28/2024]
Abstract
BACKGROUND Posttraumatic stress disorder (PTSD) has been associated with ischemic heart disease in women veterans, but evidence for associations with other cardiovascular disorders remains limited in this population. This retrospective longitudinal cohort study evaluated the association of PTSD with incident stroke/transient ischemic attack (TIA) in women veterans. METHODS AND RESULTS Veterans Health Administration electronic health records were used to identify women veterans aged ≥18 years engaged with Veterans Health Administration health care from January 1, 2000 to December 31, 2019. We identified women veterans with and without PTSD without a history of stroke or TIA at start of follow-up. Propensity score matching was used to match groups on age, race or ethnicity, traditional cardiovascular risk factors, female-specific risk factors, a range of mental and physical health conditions, and number of prior health care visits. PTSD, stroke, TIA, and risk factors used in propensity score matching were based on diagnostic codes. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% CIs for associations of PTSD with an incident stroke/TIA composite. Subanalyses considered stroke and TIA separately, plus age- and race- or ethnicity-stratified analyses were carried out. The analytic sample included 208 092 women veterans (104 046 with and 104 046 without PTSD). PTSD was associated with a greater rate of developing stroke/TIA (HR, 1.33 [95% CI, 1.25-1.42], P<0.001). This elevated risk was especially pronounced in women <50 years old and in Hispanic/Latina women. CONCLUSIONS Findings indicate a strong association of PTSD with incident stroke/TIA in women veterans. Research is needed to determine whether addressing PTSD and its downstream consequences can offset this risk.
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Affiliation(s)
- Ramin Ebrahimi
- Department of MedicineUniversity of CaliforniaLos AngelesCAUSA
- Department of MedicineVeterans Affairs (VA) Greater Los Angeles Healthcare SystemLos AngelesCAUSA
| | - Paul A. Dennis
- Department of Population Health SciencesDuke University School of MedicineDurhamNCUSA
- Durham VA Medical CenterDurhamNCUSA
| | - Carlos A. Alvarez
- Department of Pharmacy PracticeTexas Tech University Health Science CenterLubbockTXUSA
- Department of ResearchVA North Texas Health Care SystemDallasTXUSA
| | - A. Laurie Shroyer
- Department of Surgery, Renaissance School of MedicineStony Brook UniversityStony BrookNYUSA
- Northport VA Medical CenterNorthportNYUSA
| | - Jean C. Beckham
- Durham VA Medical CenterDurhamNCUSA
- Department of Psychiatry and Behavioral SciencesDuke University School of MedicineDurhamNCUSA
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Zhu AY, Roy D, Zhu Z, Sailer MO. Propensity score stratified MAP prior and posterior inference for incorporating information across multiple potentially heterogeneous data sources. J Biopharm Stat 2024; 34:190-204. [PMID: 36882957 DOI: 10.1080/10543406.2023.2181354] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 02/10/2023] [Indexed: 03/09/2023]
Abstract
Incorporation of external information is becoming increasingly common when designing clinical trials. Availability of multiple sources of information has inspired the development of methodologies that account for potential heterogeneity not only between the prospective trial and the pooled external data sources but also between the different external data sources themselves. Our approach proposes an intuitive way of handling such a scenario for the continuous outcomes setting by using propensity score-based stratification and then utilizing robust meta-analytic predictive priors for each stratum to incorporate the prior data to distinguish among different external data sources in each stratum. Through extensive simulations, our approach proves to be more efficient and less biased than the currently available methods. A real case study using clinical trials that study schizophrenia from multiple different sources is also included.
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Affiliation(s)
- Angela Yaqian Zhu
- Statistics and Decision Sciences, Janssen Research & Development, Johnson & Johnson, Raritan, New Jersey, USA
| | - Dooti Roy
- Department of Biostatistics and Data Science, Boehringer Ingelheim Pharmaceuticals Inc, Ridgefield, Connecticut, USA
| | - Zheng Zhu
- Department of Biostatistics and Data Science, Boehringer Ingelheim Pharmaceuticals Inc, Ridgefield, Connecticut, USA
| | - Martin Oliver Sailer
- Department of Biostatistics and Data Science, Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach, Germany
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Rahman MA, Victoros E, Ernest J, Davis R, Shanjana Y, Islam MR. Impact of Artificial Intelligence (AI) Technology in Healthcare Sector: A Critical Evaluation of Both Sides of the Coin. CLINICAL PATHOLOGY (THOUSAND OAKS, VENTURA COUNTY, CALIF.) 2024; 17:2632010X241226887. [PMID: 38264676 PMCID: PMC10804900 DOI: 10.1177/2632010x241226887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 12/27/2023] [Indexed: 01/25/2024]
Abstract
The influence of artificial intelligence (AI) has drastically risen in recent years, especially in the field of medicine. Its influence has spread so greatly that it is determined to become a pillar in the future medical world. A comprehensive literature search related to AI in healthcare was performed in the PubMed database and retrieved the relevant information from suitable ones. AI excels in aspects such as rapid adaptation, high diagnostic accuracy, and data management that can help improve workforce productivity. With this potential in sight, the FDA has continuously approved more machine learning (ML) software to be used by medical workers and scientists. However, there are few controversies such as increased chances of data breaches, concern for clinical implementation, and potential healthcare dilemmas. In this article, the positive and negative aspects of AI implementation in healthcare are discussed, as well as recommended some potential solutions to the potential issues at hand.
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Affiliation(s)
| | | | - Julianne Ernest
- Nesbitt School of Pharmacy Wilkes University, Wilkes-Barre, PA, USA
| | - Rob Davis
- Nesbitt School of Pharmacy Wilkes University, Wilkes-Barre, PA, USA
| | - Yeasna Shanjana
- Department of Environmental Sciences, North South University, Bashundhara, Dhaka, Bangladesh
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Dhopeshwarkar N, Yang W, Hennessy S, Rhodes JM, Cuker A, Leonard CE. Combining Super Learner with high-dimensional propensity score to improve confounding adjustment: A real-world application in chronic lymphocytic leukemia. Pharmacoepidemiol Drug Saf 2024; 33:e5678. [PMID: 37609668 PMCID: PMC10841179 DOI: 10.1002/pds.5678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 07/28/2023] [Accepted: 08/02/2023] [Indexed: 08/24/2023]
Abstract
PURPOSE High-dimensional propensity score (hdPS) is a semiautomated method that leverages a vast number of covariates available in healthcare databases to improve confounding adjustment. A novel combined Super Learner (SL)-hdPS approach was proposed to assist with selecting the number of covariates for propensity score inclusion, and was found in plasmode simulation studies to improve bias reduction and precision compared to hdPS alone. However, the approach has not been examined in the applied setting. METHODS We compared SL-hdPS's performance with that of several hdPS models, each with prespecified covariates and a different number of empirically-identified covariates, using a cohort study comparing real-world bleeding rates between ibrutinib- and bendamustine-rituximab (BR)-treated individuals with chronic lymphocytic leukemia in Optum's de-identified Clinformatics® Data Mart commercial claims database (2013-2020). We used inverse probability of treatment weighting for confounding adjustment and Cox proportional hazards regression to estimate hazard ratios (HRs) for bleeding outcomes. Parameters of interest included prespecified and empirically-identified covariate balance (absolute standardized difference [ASD] thresholds of <0.10 and <0.05) and outcome HR precision (95% confidence intervals). RESULTS We identified 2423 ibrutinib- and 1102 BR-treated individuals. Including >200 empirically-identified covariates in the hdPS model compromised covariate balance at both ASD thresholds. SL-hdPS balanced more covariates than all individual hdPS models at both ASD thresholds. The bleeding HR 95% confidence intervals were generally narrower with SL-hdPS than with individual hdPS models. CONCLUSION In a real-world application, hdPS was sensitive to the number of covariates included, while use of SL for covariate selection resulted in improved covariate balance and possibly improved precision.
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Affiliation(s)
- Neil Dhopeshwarkar
- Center for Real-World Effectiveness and Safety of Therapeutics and the Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, Pennsylvania, US)
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, Pennsylvania, US)
| | - Wei Yang
- Center for Real-World Effectiveness and Safety of Therapeutics and the Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, Pennsylvania, US)
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, Pennsylvania, US)
| | - Sean Hennessy
- Center for Real-World Effectiveness and Safety of Therapeutics and the Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, Pennsylvania, US)
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, Pennsylvania, US)
| | - Joanna M. Rhodes
- Division of Hematology/Medical Oncology, Department of Medicine, Northwell Health (New Hyde Park, New York, US)
| | - Adam Cuker
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania (Philadelphia, Pennsylvania, US)
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania (Philadelphia, Pennsylvania, US)
| | - Charles E. Leonard
- Center for Real-World Effectiveness and Safety of Therapeutics and the Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, Pennsylvania, US)
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania (Philadelphia, Pennsylvania, US)
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Xue W, Zhang X, Chan KCG, Wong RKW. RKHS-based covariate balancing for survival causal effect estimation. LIFETIME DATA ANALYSIS 2024; 30:34-58. [PMID: 36821062 DOI: 10.1007/s10985-023-09590-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 01/10/2023] [Indexed: 06/18/2023]
Abstract
Survival causal effect estimation based on right-censored data is of key interest in both survival analysis and causal inference. Propensity score weighting is one of the most popular methods in the literature. However, since it involves the inverse of propensity score estimates, its practical performance may be very unstable, especially when the covariate overlap is limited between treatment and control groups. To address this problem, a covariate balancing method is developed in this paper to estimate the counterfactual survival function. The proposed method is nonparametric and balances covariates in a reproducing kernel Hilbert space (RKHS) via weights that are counterparts of inverse propensity scores. The uniform rate of convergence for the proposed estimator is shown to be the same as that for the classical Kaplan-Meier estimator. The appealing practical performance of the proposed method is demonstrated by a simulation study as well as two real data applications to study the causal effect of smoking on survival time of stroke patients and that of endotoxin on survival time for female patients with lung cancer respectively.
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Affiliation(s)
- Wu Xue
- Meta Platforms Inc., Menlo Park, CA, 94025, USA
| | - Xiaoke Zhang
- Department of Statistics, George Washington University, Washington, DC, 20052, USA.
| | | | - Raymond K W Wong
- Department of Statistics, Texas A &M University, College Station, TX, 77843, USA
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Zhu AY, Mitra N, Hemming K, Harhay MO, Li F. Leveraging baseline covariates to analyze small cluster-randomized trials with a rare binary outcome. Biom J 2024; 66:e2200135. [PMID: 37035941 PMCID: PMC10562517 DOI: 10.1002/bimj.202200135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 11/20/2022] [Accepted: 02/08/2023] [Indexed: 04/11/2023]
Abstract
Cluster-randomized trials (CRTs) involve randomizing entire groups of participants-called clusters-to treatment arms but are often comprised of a limited or fixed number of available clusters. While covariate adjustment can account for chance imbalances between treatment arms and increase statistical efficiency in individually randomized trials, analytical methods for individual-level covariate adjustment in small CRTs have received little attention to date. In this paper, we systematically investigate, through extensive simulations, the operating characteristics of propensity score weighting and multivariable regression as two individual-level covariate adjustment strategies for estimating the participant-average causal effect in small CRTs with a rare binary outcome and identify scenarios where each adjustment strategy has a relative efficiency advantage over the other to make practical recommendations. We also examine the finite-sample performance of the bias-corrected sandwich variance estimators associated with propensity score weighting and multivariable regression for quantifying the uncertainty in estimating the participant-average treatment effect. To illustrate the methods for individual-level covariate adjustment, we reanalyze a recent CRT testing a sedation protocol in 31 pediatric intensive care units.
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Affiliation(s)
- Angela Y. Zhu
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States of America
| | - Nandita Mitra
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States of America
| | - Karla Hemming
- Department of Public Health, Epidemiology, and Biostatistics, University of Birmingham Institute of Applied Health Research, Birmingham B15 2TT, United Kingdom
| | - Michael O. Harhay
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States of America
| | - Fan Li
- Department of Biostatistics, Yale School of Public Health, New Haven, CT 06510, United States of America
- Center for Methods in Implementation and Prevention Science, Yale School of Public Health, New Haven, CT 06510, United States of America
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14
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Zang C, Zhang H, Xu J, Zhang H, Fouladvand S, Havaldar S, Cheng F, Chen K, Chen Y, Glicksberg BS, Chen J, Bian J, Wang F. High-throughput target trial emulation for Alzheimer's disease drug repurposing with real-world data. Nat Commun 2023; 14:8180. [PMID: 38081829 PMCID: PMC10713627 DOI: 10.1038/s41467-023-43929-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 11/24/2023] [Indexed: 12/18/2023] Open
Abstract
Target trial emulation is the process of mimicking target randomized trials using real-world data, where effective confounding control for unbiased treatment effect estimation remains a main challenge. Although various approaches have been proposed for this challenge, a systematic evaluation is still lacking. Here we emulated trials for thousands of medications from two large-scale real-world data warehouses, covering over 10 years of clinical records for over 170 million patients, aiming to identify new indications of approved drugs for Alzheimer's disease. We assessed different propensity score models under the inverse probability of treatment weighting framework and suggested a model selection strategy for improved baseline covariate balancing. We also found that the deep learning-based propensity score model did not necessarily outperform logistic regression-based methods in covariate balancing. Finally, we highlighted five top-ranked drugs (pantoprazole, gabapentin, atorvastatin, fluticasone, and omeprazole) originally intended for other indications with potential benefits for Alzheimer's patients.
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Affiliation(s)
- Chengxi Zang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, New York, NY, USA
| | - Hao Zhang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA
| | - Jie Xu
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Hansi Zhang
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Sajjad Fouladvand
- Institude for Biomedical Informatics (IBI) and Department of Computer Science, University of Kentucky, Lexington, KY, USA
| | - Shreyas Havaldar
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Kun Chen
- Department of Statistics, University of Connecticut, Storrs, CT, USA
| | - Yong Chen
- Department of Biostatistics, Epidemiology and Informatics (DBEI), the Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Benjamin S Glicksberg
- Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jin Chen
- Institude for Biomedical Informatics (IBI) and Department of Computer Science, University of Kentucky, Lexington, KY, USA
| | - Jiang Bian
- Department of Health Outcomes & Biomedical Informatics, University of Florida, Gainesville, FL, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA.
- Institute of Artificial Intelligence for Digital Health, Weill Cornell Medicine, New York, NY, USA.
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15
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Barros GWF, Eriksson M, Häggström J. Performance of modeling and balancing approach methods when using weights to estimate treatment effects in observational time-to-event settings. PLoS One 2023; 18:e0289316. [PMID: 38060567 PMCID: PMC10703278 DOI: 10.1371/journal.pone.0289316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 07/16/2023] [Indexed: 12/18/2023] Open
Abstract
In observational studies weighting techniques are often used to overcome bias due to confounding. Modeling approaches, such as inverse propensity score weighting, are popular, but often rely on the correct specification of a parametric model wherein neither balance nor stability are targeted. More recently, balancing approach methods that directly target covariate imbalances have been proposed, and these allow the researcher to explicitly set the desired balance constraints. In this study, we evaluate the finite sample properties of different modeling and balancing approach methods, when estimating the marginal hazard ratio, through Monte Carlo simulations. The use of the different methods is also illustrated by analyzing data from the Swedish stroke register to estimate the effect of prescribing oral anticoagulants on time to recurrent stroke or death in stroke patients with atrial fibrillation. In simulated scenarios with good overlap and low or no model misspecification the balancing approach methods performed similarly to the modeling approach methods. In scenarios with bad overlap and model misspecification, the modeling approach method incorporating variable selection performed better than the other methods. The results indicate that it is valuable to use methods that target covariate balance when estimating marginal hazard ratios, but this does not in itself guarantee good performance in situations with, e.g., poor overlap, high censoring, or misspecified models/balance constraints.
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Affiliation(s)
- Guilherme W. F. Barros
- Department of Statistics, Umeå School of Business, Economics and Statistics, Umeå University, Umeå, Sweden
| | - Marie Eriksson
- Department of Statistics, Umeå School of Business, Economics and Statistics, Umeå University, Umeå, Sweden
| | - Jenny Häggström
- Department of Statistics, Umeå School of Business, Economics and Statistics, Umeå University, Umeå, Sweden
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16
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Hauptmann T, Fellenz S, Nathan L, Tüscher O, Kramer S. Discriminative machine learning for maximal representative subsampling. Sci Rep 2023; 13:20925. [PMID: 38017053 PMCID: PMC10684887 DOI: 10.1038/s41598-023-48177-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 11/23/2023] [Indexed: 11/30/2023] Open
Abstract
Biased population samples pose a prevalent problem in the social sciences. Therefore, we present two novel methods that are based on positive-unlabeled learning to mitigate bias. Both methods leverage auxiliary information from a representative data set and train machine learning classifiers to determine the sample weights. The first method, named maximum representative subsampling (MRS), uses a classifier to iteratively remove instances, by assigning a sample weight of 0, from the biased data set until it aligns with the representative one. The second method is a variant of MRS - Soft-MRS - that iteratively adapts sample weights instead of removing samples completely. To assess the effectiveness of our approach, we induced artificial bias in a public census data set and examined the corrected estimates. We compare the performance of our methods against existing techniques, evaluating the ability of sample weights created with Soft-MRS or MRS to minimize differences and improve downstream classification tasks. Lastly, we demonstrate the applicability of the proposed methods in a real-world study of resilience research, exploring the influence of resilience on voting behavior. Through our work, we address the issue of bias in social science, amongst others, and provide a versatile methodology for bias reduction based on machine learning. Based on our experiments, we recommend to use MRS for downstream classification tasks and Soft-MRS for downstream tasks where the relative bias of the dependent variable is relevant.
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Affiliation(s)
- Tony Hauptmann
- Institute of Computer Science, Johannes Gutenberg University Mainz, Mainz, Germany.
| | - Sophie Fellenz
- Institute of Computer Science, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Laksan Nathan
- Institute of Computer Science, Johannes Gutenberg University Mainz, Mainz, Germany
| | - Oliver Tüscher
- The Leibniz Institute for Resilience Research, Mainz, Germany
- Department of Psychiatry and Psychotherapy, University Medical Center Mainz, Mainz, Germany
| | - Stefan Kramer
- Institute of Computer Science, Johannes Gutenberg University Mainz, Mainz, Germany
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17
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Nordin NI, Mustafa WA, Lola MS, Madi EN, Kamil AA, Nasution MD, K. Abdul Hamid AA, Zainuddin NH, Aruchunan E, Abdullah MT. Enhancing COVID-19 Classification Accuracy with a Hybrid SVM-LR Model. Bioengineering (Basel) 2023; 10:1318. [PMID: 38002441 PMCID: PMC10669812 DOI: 10.3390/bioengineering10111318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 10/03/2023] [Accepted: 10/09/2023] [Indexed: 11/26/2023] Open
Abstract
Support ector achine (SVM) is a newer machine learning algorithm for classification, while logistic regression (LR) is an older statistical classification method. Despite the numerous studies contrasting SVM and LR, new improvements such as bagging and ensemble have been applied to them since these comparisons were made. This study proposes a new hybrid model based on SVM and LR for predicting small events per variable (EPV). The performance of the hybrid, SVM, and LR models with different EPV values was evaluated using COVID-19 data from December 2019 to May 2020 provided by the WHO. The study found that the hybrid model had better classification performance than SVM and LR in terms of accuracy, mean squared error (MSE), and root mean squared error (RMSE) for different EPV values. This hybrid model is particularly important for medical authorities and practitioners working in the face of future pandemics.
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Affiliation(s)
- Noor Ilanie Nordin
- Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia or (N.I.N.); (A.A.K.A.H.)
- Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Kelantan, Bukit Ilmu, Machang 18500, Kelantan, Malaysia
| | - Wan Azani Mustafa
- Faculty of Electrical Engineering & Technology, Pauh Putra Campus, Universiti Malaysia Perlis (UniMAP), Arau 02600, Perlis, Malaysia
- Centre of Excellence for Advanced Computing, Pauh Putra Campus, Universiti Malaysia Perlis (UniMAP), Arau 02600, Perlis, Malaysia
| | - Muhamad Safiih Lola
- Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia or (N.I.N.); (A.A.K.A.H.)
- Special Interest Group on Modeling and Data Analytics (SIGMDA), Universiti Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia
| | - Elissa Nadia Madi
- Faculty of Informatics and Computing, Universiti Sultan Zainal Abidin (UniSZA), Besut Campus, Besut 22200, Terengganu, Malaysia;
| | - Anton Abdulbasah Kamil
- Faculty of Economics, Administrative and Social Sciences, Istanbul Gelisim University, Cihangir Mah. Şehit Jandarma Komando Er Hakan Öner Sk. No:1 Avcılar, İstanbul 34310, Turkey;
| | - Marah Doly Nasution
- Faculty of Teacher and Education, University Muhammadiyah Sumatera Utara, Jl. Kapten Muchtar Basri No.3, Glugur Darat II, Kec. Medan Tim., Kota Medan 20238, Sumatera Utara, Indonesia;
| | - Abdul Aziz K. Abdul Hamid
- Faculty of Ocean Engineering Technology and Informatics, Universiti Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia or (N.I.N.); (A.A.K.A.H.)
- Special Interest Group on Applied Informatics and Intelligent Applications (AINIA), Universiti Malaysia Terengganu, Kuala Nerus 21030, Terengganu, Malaysia
| | - Nurul Hila Zainuddin
- Mathematics Department, Faculty of Science and Mathematics, Universiti Pendidikan Sultan Idris, Tanjong Malim 53900, Perak Darul Ridzuan, Malaysia;
| | - Elayaraja Aruchunan
- Department of Decision Science, Faculty of Business and Economics, University Malaya, Kuala Lumpur 50603, Malaysia;
| | - Mohd Tajuddin Abdullah
- Fellow Academy of Sciences Malaysia, Level 20, West Wing Tingkat 20, Menara MATRADE, Jalan Sultan Haji Ahmad Shah, Kuala Lumpur 50480, Malaysia;
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18
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Wang C, Wei K, Huang C, Yu Y, Qin G. An improved multiply robust estimator for the average treatment effect. BMC Med Res Methodol 2023; 23:231. [PMID: 37821829 PMCID: PMC10568861 DOI: 10.1186/s12874-023-02056-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 10/01/2023] [Indexed: 10/13/2023] Open
Abstract
BACKGROUND In observational studies, double robust or multiply robust (MR) approaches provide more protection from model misspecification than the inverse probability weighting and g-computation for estimating the average treatment effect (ATE). However, the approaches are based on parametric models, leading to biased estimates when all models are incorrectly specified. Nonparametric methods, such as machine learning or nonparametric double robust approaches, are robust to model misspecification, but the efficiency of nonparametric methods is low. METHOD In the study, we proposed an improved MR method combining parametric and nonparametric models based on the previous MR method (Han, JASA 109(507):1159-73, 2014) to improve the robustness to model misspecification and the efficiency. We performed comprehensive simulations to evaluate the performance of the proposed method. RESULTS Our simulation study showed that the MR estimators with only outcome regression (OR) models, where one of the models was a nonparametric model, were the most recommended because of the robustness to model misspecification and the lowest root mean square error (RMSE) when including a correct parametric OR model. And the performance of the recommended estimators was comparative, even if all parametric models were misspecified. As an application, the proposed method was used to estimate the effect of social activity on depression levels in the China Health and Retirement Longitudinal Study dataset. CONCLUSIONS The proposed estimator with nonparametric and parametric models is more robust to model misspecification.
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Affiliation(s)
- Ce Wang
- Department of Biostatistics, Key Laboratory for Health Technology Assessment, National Commission of Health, Key Laboratory of Public Health Safety of Ministry of Education, School of Public Health, Fudan University, Shanghai, China
| | - Kecheng Wei
- Department of Biostatistics, Key Laboratory for Health Technology Assessment, National Commission of Health, Key Laboratory of Public Health Safety of Ministry of Education, School of Public Health, Fudan University, Shanghai, China
| | - Chen Huang
- Department of Biostatistics, Key Laboratory for Health Technology Assessment, National Commission of Health, Key Laboratory of Public Health Safety of Ministry of Education, School of Public Health, Fudan University, Shanghai, China
| | - Yongfu Yu
- Department of Biostatistics, Key Laboratory for Health Technology Assessment, National Commission of Health, Key Laboratory of Public Health Safety of Ministry of Education, School of Public Health, Fudan University, Shanghai, China.
- Shanghai Institute of Infectious Disease and Biosecurity, Shanghai, China.
| | - Guoyou Qin
- Department of Biostatistics, Key Laboratory for Health Technology Assessment, National Commission of Health, Key Laboratory of Public Health Safety of Ministry of Education, School of Public Health, Fudan University, Shanghai, China.
- Shanghai Institute of Infectious Disease and Biosecurity, Shanghai, China.
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19
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Chakraborty N, Lawrence A, Campbell R, Yang R, Hammamieh R. Biomarker discovery process at binomial decision point (2BDP): Analytical pipeline to construct biomarker panel. Comput Struct Biotechnol J 2023; 21:4729-4742. [PMID: 37822559 PMCID: PMC10562676 DOI: 10.1016/j.csbj.2023.09.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 09/21/2023] [Accepted: 09/21/2023] [Indexed: 10/13/2023] Open
Abstract
A clinical incident is typically manifested by several molecular events; therefore, it seems logical that a successful diagnosis, prognosis, or stratification of a clinical landmark require multiple biomarkers. In this report, we presented a machine learning pipeline, namely "Biomarker discovery process at binomial decision point" (2BDP) that took an integrative approach in systematically curating independent variables (e.g., multiple molecular markers) to explain an output variable (e.g., clinical landmark) of binary in nature. In a logical sequence, 2BDP includes feature selection, unsupervised model development and cross validation. In the present work, the efficiency of 2BDP was demonstrated by finding three biomarker panels that independently explained three stages of Alzheimer's disease (AD) marked as Braak stages I, II and III, respectively. We designed three assortments from the entire cohort based on these Braak stages; subsequently, each assortment was split into two populations at Braak score I, II or III. 2BDP systematically integrated random forest and logistic regression fitting model to find biomarker panels with minimum features that explained these three assortments, e.g., significantly differentiated two populations segregated by Braak stage I, II or III, respectively. Thereafter, the efficacies of these panels were measured by the area under the curve (AUC) values of the receiver operating characteristic (ROC) plot. The AUC-ROC was calculated by two cross-validation methods. Final set of gene markers was a mix of novel and a priori established AD signatures. These markers were weighted by unique coefficients and linearly connected in a group of 2-10 to explain Braak stage I, II or III by AUC ≥ 0.8. Small sample size and a lack of distinctly recruited Training and Test sets were the limitations of the present undertaking; yet 2BDP demonstrated its capability to curate a panel of optimum numbers of biomarkers to describe the outcome variable with high efficacy.
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Affiliation(s)
- Nabarun Chakraborty
- Medical Readiness Systems Biology, Center for Military Psychiatry and Neuroscience (CMPN), Walter Reed Army Institute of Research, Silver Spring, MD, USA
| | - Alexander Lawrence
- Medical Readiness Systems Biology, Center for Military Psychiatry and Neuroscience (CMPN), Walter Reed Army Institute of Research, Silver Spring, MD, USA
- ORISE, Walter Reed Army Institute of Research, Silver Spring, MD, USA
| | - Ross Campbell
- Medical Readiness Systems Biology, Center for Military Psychiatry and Neuroscience (CMPN), Walter Reed Army Institute of Research, Silver Spring, MD, USA
- Geneva Foundation, Walter Reed Army Institute of Research, Silver Spring, MD, USA
| | - Ruoting Yang
- Medical Readiness Systems Biology, Center for Military Psychiatry and Neuroscience (CMPN), Walter Reed Army Institute of Research, Silver Spring, MD, USA
| | - Rasha Hammamieh
- Medical Readiness Systems Biology, Center for Military Psychiatry and Neuroscience (CMPN), Walter Reed Army Institute of Research, Silver Spring, MD, USA
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20
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Byeon S, Lee W. Directed acyclic graphs for clinical research: a tutorial. JOURNAL OF MINIMALLY INVASIVE SURGERY 2023; 26:97-107. [PMID: 37712307 PMCID: PMC10505364 DOI: 10.7602/jmis.2023.26.3.97] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 06/26/2023] [Indexed: 09/16/2023]
Abstract
Directed acyclic graphs (DAGs) are useful tools for visualizing the hypothesized causal structures in an intuitive way and selecting relevant confounders in causal inference. However, in spite of their increasing use in clinical and surgical research, the causal graphs might also be misused by a lack of understanding of the central principles. In this article, we aim to introduce the basic terminology and fundamental rules of DAGs, and DAGitty, a user-friendly program that easily displays DAGs. Specifically, we describe how to determine variables that should or should not be adjusted based on the backdoor criterion with examples. In addition, the occurrence of the various types of biases is discussed with caveats, including the problem caused by the traditional approach using p-values for confounder selection. Moreover, a detailed guide to DAGitty is provided with practical examples regarding minimally invasive surgery. Essentially, the primary benefit of DAGs is to aid researchers in clarifying the research questions and the corresponding designs based on the domain knowledge. With these strengths, we propose that the use of DAGs may contribute to rigorous research designs, and lead to transparency and reproducibility in research on minimally invasive surgery.
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Affiliation(s)
- Sangmin Byeon
- Institute of Health & Environment, Seoul National University, Seoul, Korea
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Korea
| | - Woojoo Lee
- Institute of Health & Environment, Seoul National University, Seoul, Korea
- Department of Public Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, Korea
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21
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Kaizar E, Lin CY, Faries D, Johnston J. Reweighting estimators to extend the external validity of clinical trials: methodological considerations. J Biopharm Stat 2023; 33:515-543. [PMID: 36688658 DOI: 10.1080/10543406.2022.2162067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 12/10/2022] [Indexed: 01/24/2023]
Abstract
Methods to extend the strong internal validity of randomized controlled trials to reliably estimate treatment effects in target populations are gaining attention. This paper enumerates steps recommended for undertaking such extended inference, discusses currently viable choices for each one, and provides recommendations. We demonstrate a complete extended inference from a clinical trial studying a pharmaceutical treatment for Alzheimer's disease (AD) to a realistic target population of European residents diagnosed with AD. This case study highlights approaches to overcoming practical difficulties and demonstrates limitations of reliably extending inference from a trial to a real-world population.
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Affiliation(s)
- Eloise Kaizar
- Department of Statistics, Ohio State University, Columbus, Ohio, USA
| | - Chen-Yen Lin
- FSP Biometrics, Syneos Health, Toronto, Ontario, Canada
| | - Douglas Faries
- Real World Analytics, Eli Lilly and Company, Indianapolis, Indiana, USA
| | - Joseph Johnston
- Value, Evidence, and Outcomes, Eli Lilly and Company, Indianapolis, Indiana, USA
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22
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Weymann D, Chan B, Regier DA. Genetic matching for time-dependent treatments: a longitudinal extension and simulation study. BMC Med Res Methodol 2023; 23:181. [PMID: 37559105 PMCID: PMC10413721 DOI: 10.1186/s12874-023-01995-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 07/21/2023] [Indexed: 08/11/2023] Open
Abstract
BACKGROUND Longitudinal matching can mitigate confounding in observational, real-world studies of time-dependent treatments. To date, these methods have required iterative, manual re-specifications to achieve covariate balance. We propose a longitudinal extension of genetic matching, a machine learning approach that automates balancing of covariate histories. We examine performance by comparing the proposed extension against baseline propensity score matching and time-dependent propensity score matching. METHODS To evaluate comparative performance, we developed a Monte Carlo simulation framework that reflects a static treatment assigned at multiple time points. Data generation considers a treatment assignment model, a continuous outcome model, and underlying covariates. In simulation, we generated 1,000 datasets, each consisting of 1,000 subjects, and applied: (1) nearest neighbour matching on time-invariant, baseline propensity scores; (2) sequential risk set matching on time-dependent propensity scores; and (3) longitudinal genetic matching on time-dependent covariates. To measure comparative performance, we estimated covariate balance, efficiency, bias, and root mean squared error (RMSE) of treatment effect estimates. In scenario analysis, we varied underlying assumptions for assumed covariate distributions, correlations, treatment assignment models, and outcome models. RESULTS In all scenarios, baseline propensity score matching resulted in biased effect estimation in the presence of time-dependent confounding, with mean bias ranging from 29.7% to 37.2%. In contrast, time-dependent propensity score matching and longitudinal genetic matching achieved stronger covariate balance and yielded less biased estimation, with mean bias ranging from 0.7% to 13.7%. Across scenarios, longitudinal genetic matching achieved similar or better performance than time-dependent propensity score matching without requiring manual re-specifications or normality of covariates. CONCLUSIONS While the most appropriate longitudinal method will depend on research questions and underlying data patterns, our study can help guide these decisions. Simulation results demonstrate the validity of our longitudinal genetic matching approach for supporting future real-world assessments of treatments accessible at multiple time points.
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Affiliation(s)
| | - Brandon Chan
- Cancer Control Research, BC Cancer, Vancouver, Canada
| | - Dean A Regier
- Cancer Control Research, BC Cancer, Vancouver, Canada
- School of Population and Public Health, University of British Columbia, Vancouver, Canada
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23
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Mangino AA, Bolin JH, Finch WH. Fixed Effects or Mixed Effects Classifiers? Evidence From Simulated and Archival Data. EDUCATIONAL AND PSYCHOLOGICAL MEASUREMENT 2023; 83:710-739. [PMID: 37398843 PMCID: PMC10311958 DOI: 10.1177/00131644221108180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
This study seeks to compare fixed and mixed effects models for the purposes of predictive classification in the presence of multilevel data. The first part of the study utilizes a Monte Carlo simulation to compare fixed and mixed effects logistic regression and random forests. An applied examination of the prediction of student retention in the public-use U.S. PISA data set was considered to verify the simulation findings. Results of this study indicate fixed effects models performed comparably with mixed effects models across both the simulation and PISA examinations. Results broadly suggest that researchers should be cognizant of the type of predictors and data structure being used, as these factors carried more weight than did the model type.
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Affiliation(s)
- Anthony A. Mangino
- Ball State University, Muncie, IN, USA
- University of Kentucky, Lexington, USA
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24
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Garcia Santa Cruz B, Husch A, Hertel F. Machine learning models for diagnosis and prognosis of Parkinson's disease using brain imaging: general overview, main challenges, and future directions. Front Aging Neurosci 2023; 15:1216163. [PMID: 37539346 PMCID: PMC10394631 DOI: 10.3389/fnagi.2023.1216163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 06/28/2023] [Indexed: 08/05/2023] Open
Abstract
Parkinson's disease (PD) is a progressive and complex neurodegenerative disorder associated with age that affects motor and cognitive functions. As there is currently no cure, early diagnosis and accurate prognosis are essential to increase the effectiveness of treatment and control its symptoms. Medical imaging, specifically magnetic resonance imaging (MRI), has emerged as a valuable tool for developing support systems to assist in diagnosis and prognosis. The current literature aims to improve understanding of the disease's structural and functional manifestations in the brain. By applying artificial intelligence to neuroimaging, such as deep learning (DL) and other machine learning (ML) techniques, previously unknown relationships and patterns can be revealed in this high-dimensional data. However, several issues must be addressed before these solutions can be safely integrated into clinical practice. This review provides a comprehensive overview of recent ML techniques analyzed for the automatic diagnosis and prognosis of PD in brain MRI. The main challenges in applying ML to medical diagnosis and its implications for PD are also addressed, including current limitations for safe translation into hospitals. These challenges are analyzed at three levels: disease-specific, task-specific, and technology-specific. Finally, potential future directions for each challenge and future perspectives are discussed.
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Affiliation(s)
| | - Andreas Husch
- Imaging AI Group, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Frank Hertel
- National Department of Neurosurgery, Centre Hospitalier de Luxembourg, Luxembourg, Luxembourg
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25
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Vora LK, Gholap AD, Jetha K, Thakur RRS, Solanki HK, Chavda VP. Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design. Pharmaceutics 2023; 15:1916. [PMID: 37514102 PMCID: PMC10385763 DOI: 10.3390/pharmaceutics15071916] [Citation(s) in RCA: 50] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 06/28/2023] [Accepted: 07/04/2023] [Indexed: 07/30/2023] Open
Abstract
Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes. Machine learning algorithms assist in experimental design and can predict the pharmacokinetics and toxicity of drug candidates. This capability enables the prioritization and optimization of lead compounds, reducing the need for extensive and costly animal testing. Personalized medicine approaches can be facilitated through AI algorithms that analyze real-world patient data, leading to more effective treatment outcomes and improved patient adherence. This comprehensive review explores the wide-ranging applications of AI in drug discovery, drug delivery dosage form designs, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies. This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care.
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Affiliation(s)
- Lalitkumar K Vora
- School of Pharmacy, Queen's University Belfast, 97 Lisburn Road, Belfast BT9 7BL, UK
| | - Amol D Gholap
- Department of Pharmaceutics, St. John Institute of Pharmacy and Research, Palghar 401404, Maharashtra, India
| | - Keshava Jetha
- Department of Pharmaceutics and Pharmaceutical Technology, L. M. College of Pharmacy, Ahmedabad 380009, Gujarat, India
- Ph.D. Section, Gujarat Technological University, Ahmedabad 382424, Gujarat, India
| | | | - Hetvi K Solanki
- Pharmacy Section, L. M. College of Pharmacy, Ahmedabad 380009, Gujarat, India
| | - Vivek P Chavda
- Department of Pharmaceutics and Pharmaceutical Technology, L. M. College of Pharmacy, Ahmedabad 380009, Gujarat, India
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26
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Brand JE, Zhou X, Xie Y. Recent Developments in Causal Inference and Machine Learning. ANNUAL REVIEW OF SOCIOLOGY 2023; 49:81-110. [PMID: 38911356 PMCID: PMC11192458 DOI: 10.1146/annurev-soc-030420-015345] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/25/2024]
Abstract
This article reviews recent advances in causal inference relevant to sociology. We focus on a selective subset of contributions aligning with four broad topics: causal effect identification and estimation in general, causal effect heterogeneity, causal effect mediation, and temporal and spatial interference. We describe how machine learning, as an estimation strategy, can be effectively combined with causal inference, which has been traditionally concerned with identification. The incorporation of machine learning in causal inference enables researchers to better address potential biases in estimating causal effects and uncover heterogeneous causal effects. Uncovering sources of effect heterogeneity is key for generalizing to populations beyond those under study. While sociology has long emphasized the importance of causal mechanisms, historical and life-cycle variation, and social contexts involving network interactions, recent conceptual and computational advances facilitate more principled estimation of causal effects under these settings. We encourage sociologists to incorporate these insights into their empirical research.
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Affiliation(s)
- Jennie E Brand
- Department of Sociology, Department of Statistics, California Center for Population Research, and Center for Social Statistics, University of California, Los Angeles, California, USA
| | - Xiang Zhou
- Department of Sociology, Harvard University, Cambridge, Massachusetts, USA
| | - Yu Xie
- Department of Sociology, Princeton University, Princeton, New Jersey, USA
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27
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Zhang H, Zhou X, Lv X, Xu X, Weng Q, Lei K. Exploration of the factors that influence total phosphorus in surface water and an evaluation of surface water vulnerability based on an advanced algorithm and traditional index method. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 342:118155. [PMID: 37209649 DOI: 10.1016/j.jenvman.2023.118155] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 04/27/2023] [Accepted: 05/10/2023] [Indexed: 05/22/2023]
Abstract
Due to the continuous influence of human activities, phosphorus pollution in surface water has become a persistent problem that needs to be addressed since phosphorous entails certain risks and degrees of damage to ecosystems and humans. The presence and accumulation of total phosphorus (TP) concentrations in surface waters is the result of a combined effect of many natural and anthropogenic factors, and it is often difficult to intuitively identify the individual importance of each factor in regard to the pollution of the aquatic environment. Considering these issues, this study provides a new methodology to better understand the vulnerability of surface water to TP pollution and the factors that influence TP pollution through the application of two modeling approaches. This includes the boosted regression tree (BRT), an advanced machine learning method, and the traditional comprehensive index method (CIM). Different factors, such as natural variables (including slope, soil texture, normalized difference vegetation index (NDVI), precipitation, and drainage density) and point and nonpoint source anthropogenic factors were included to model the vulnerability of surface water to TP pollution. Two methods were used to produce a vulnerability map of surface water to TP pollution. Pearson correlation analysis was used to validate the two vulnerability assessment methods. The results showed that BRT was more strongly correlated than CIM. In addition, the importance ranking results showed that slope, precipitation, NDVI, decentralized livestock farming and soil texture had a greater influence on TP pollution. Industrial activities, scale livestock farming and population density, which are all contributing sources of pollution, were all relatively less important. The introduced methodology can be used to quickly identify the area most vulnerable to TP pollution and to develop problem specific adaptive policies and measures to reduce the damage from TP pollution.
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Affiliation(s)
- Hua Zhang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, PR China; College of Water Sciences, Beijing Normal University, Beijing, 100875, PR China
| | - Xiyin Zhou
- School of Systems Science, Beijing Normal University, Beijing, 100875, PR China
| | - Xubo Lv
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, PR China
| | - Xiangqin Xu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, PR China
| | - Qiaoran Weng
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, PR China
| | - Kun Lei
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, PR China.
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28
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Wolfson J, Venkatasubramaniam A. Best (but oft forgotten) statistical practices: Measuring real-world intervention effectiveness using electronic health data. Am J Clin Nutr 2023:S0002-9165(23)48899-5. [PMID: 37141992 DOI: 10.1016/j.ajcnut.2023.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 04/26/2023] [Accepted: 05/01/2023] [Indexed: 05/06/2023] Open
Abstract
The evidence base supporting the use of most interventions consists primarily of data from randomized controlled trials (RCTs), but how and to whom interventions are delivered in clinical practice may differ substantially from these foundational RCTs. With the increasing availability of electronic health data, it is now feasible to study the "real-world" effectiveness of a wide range of interventions. However, real-world intervention effectiveness studies using electronic health data face many challenges including data quality, selection bias, confounding by indication, and lack of generalizability. In this article, we describe the key barriers to generating high-quality evidence from real-world intervention effectiveness studies and suggest statistical best practices for addressing them.
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Affiliation(s)
- Julian Wolfson
- Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
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29
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Design of a five-layers multi-energy X-ray imaging detector for material sorting. Appl Radiat Isot 2023; 194:110711. [PMID: 36804701 DOI: 10.1016/j.apradiso.2023.110711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 01/31/2023] [Accepted: 02/01/2023] [Indexed: 02/05/2023]
Abstract
X-ray transmission imaging (XRT) is widely used for sorting materials. However, conventional single-energy and dual-energy X-ray systems have poor ability to discriminate between materials with similar atomic number (Z), and the count rate of available multi-energy XRT detectors could not support high-speed industrial applications. This paper presents the design of a detector that can potentiality achieve high-speed multi-energy X-ray imaging using Geant4 simulations. This detector consisted of five detection layers (with three scintillator materials: CsI, GOS and CdWO4), two metal filters, which allows X-ray imaging at five energies. Validation simulation showed that the 15% more accurate than a dual-layers detector in the classification of Mg and Al alloys.
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30
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Brüggmann D, Kreyenfeld M. Earnings Trajectories After Divorce: The Legacies of the Earner Model During Marriage. POPULATION RESEARCH AND POLICY REVIEW 2023. [DOI: 10.1007/s11113-023-09756-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
AbstractDivorce marks the legal endpoint of a marital union. While divorce is increasingly seen as a ‘clean break’, the past marital history of the couple may nevertheless shape their present conditions. In particular, there may be a legacy of a highly gendered division of labour during marriage that may affect the ex-spouses’ earning trajectories beyond the date of divorce. Using register data from the German Pension Fund, we examine the earning trajectories of heterosexual couples who filed for a divorce in 2013 (24,616 men and 24,616 women). Using fixed-effects and matching techniques, we compare the earning trajectories of divorcees with those of a control group of married persons in the period spanning two years before and two years after divorce. In particular, we examine how the earner models divorcees followed during marriage shaped their future earning trajectories. Our results show that, on average, the earnings of a divorced woman in a male breadwinner constellation increased after divorce, while the earnings of her male ex-spouse declined. Nevertheless, large gender differences in earnings persisted: 2 years after separation, a divorced woman who had been in a male breadwinner constellation was, on average, earning 72% less than her ex-spouse. We discuss our findings against the background of recent policy reforms in Germany, which assume that ex-partners should be economically ‘self-reliant’ after divorce.
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31
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Ségalas C, Leyrat C, Carpenter JR, Williamson E. Propensity score matching after multiple imputation when a confounder has missing data. Stat Med 2023; 42:1082-1095. [PMID: 36695043 PMCID: PMC10946973 DOI: 10.1002/sim.9658] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 12/08/2022] [Accepted: 01/04/2023] [Indexed: 01/26/2023]
Abstract
One of the main challenges when using observational data for causal inference is the presence of confounding. A classic approach to account for confounding is the use of propensity score techniques that provide consistent estimators of the causal treatment effect under four common identifiability assumptions for causal effects, including that of no unmeasured confounding. Propensity score matching is a very popular approach which, in its simplest form, involves matching each treated patient to an untreated patient with a similar estimated propensity score, that is, probability of receiving the treatment. The treatment effect can then be estimated by comparing treated and untreated patients within the matched dataset. When missing data arises, a popular approach is to apply multiple imputation to handle the missingness. The combination of propensity score matching and multiple imputation is increasingly applied in practice. However, in this article we demonstrate that combining multiple imputation and propensity score matching can lead to over-coverage of the confidence interval for the treatment effect estimate. We explore the cause of this over-coverage and we evaluate, in this context, the performance of a correction to Rubin's rules for multiple imputation proposed by finding that this correction removes the over-coverage.
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Affiliation(s)
- Corentin Ségalas
- Department of Medical StatisticsLondon School of Hygiene and Tropical MedicineLondonUK
- Université Paris‐CitéCentre of Epidemiology and Statistics (CRESS) InsermParisFrance
| | - Clémence Leyrat
- Department of Medical StatisticsLondon School of Hygiene and Tropical MedicineLondonUK
| | - James R. Carpenter
- Department of Medical StatisticsLondon School of Hygiene and Tropical MedicineLondonUK
- MRC Clinical Trials Unit at UCLUCLLondonUK
| | - Elizabeth Williamson
- Department of Medical StatisticsLondon School of Hygiene and Tropical MedicineLondonUK
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32
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Pickreign JD. Iterative proportional fitting as a balancing method in observational studies. HEALTH SERVICES AND OUTCOMES RESEARCH METHODOLOGY 2023. [DOI: 10.1007/s10742-023-00304-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023]
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33
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Mitra R. A latent class model to multiply impute missing treatment indicators in observational studies when inferences of the treatment effect are made using propensity score matching. Biom J 2023; 65:e2100284. [PMID: 36418159 DOI: 10.1002/bimj.202100284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 08/30/2022] [Accepted: 09/22/2022] [Indexed: 11/25/2022]
Abstract
Analysts often estimate treatment effects in observational studies using propensity score matching techniques. When there are missing covariate values, analysts can multiply impute the missing data to create m completed data sets. Analysts can then estimate propensity scores on each of the completed data sets, and use these to estimate treatment effects. However, there has been relatively little attention on developing imputation models to deal with the additional problem of missing treatment indicators, perhaps due to the consequences of generating implausible imputations. However, simply ignoring the missing treatment values, akin to a complete case analysis, could also lead to problems when estimating treatment effects. We propose a latent class model to multiply impute missing treatment indicators. We illustrate its performance through simulations and with data taken from a study on determinants of children's cognitive development. This approach is seen to obtain treatment effect estimates closer to the true treatment effect than when employing conventional imputation procedures as well as compared to a complete case analysis.
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Affiliation(s)
- Robin Mitra
- Department of Statistical Science, University College London, London, WC1E 6BT, UK
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34
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Yang S, Du P, Feng X, He D, Chen Y, Zhong LLD, Yan X, Luo J. Propensity score analysis with missing data using a multi-task neural network. BMC Med Res Methodol 2023; 23:41. [PMID: 36793016 PMCID: PMC9930709 DOI: 10.1186/s12874-023-01847-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Accepted: 01/20/2023] [Indexed: 02/17/2023] Open
Abstract
BACKGROUND Propensity score analysis is increasingly used to control for confounding factors in observational studies. Unfortunately, unavoidable missing values make estimating propensity scores extremely challenging. We propose a new method for estimating propensity scores in data with missing values. MATERIALS AND METHODS Both simulated and real-world datasets are used in our experiments. The simulated datasets were constructed under 2 scenarios, the presence (T = 1) and the absence (T = 0) of the true effect. The real-world dataset comes from LaLonde's employment training program. We construct missing data with varying degrees of missing rates under three missing mechanisms: MAR, MCAR, and MNAR. Then we compare MTNN with 2 other traditional methods in different scenarios. The experiments in each scenario were repeated 20,000 times. Our code is publicly available at https://github.com/ljwa2323/MTNN . RESULTS Under the three missing mechanisms of MAR, MCAR and MNAR, the RMSE between the effect and the true effect estimated by our proposed method is the smallest in simulations and in real-world data. Furthermore, the standard deviation of the effect estimated by our method is the smallest. In situations where the missing rate is low, the estimation of our method is more accurate. CONCLUSIONS MTNN can perform propensity score estimation and missing value filling at the same time through shared hidden layers and joint learning, which solves the dilemma of traditional methods and is very suitable for estimating true effects in samples with missing values. The method is expected to be broadly generalized and applied to real-world observational studies.
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Affiliation(s)
- Shu Yang
- grid.411304.30000 0001 0376 205XSchool of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Peipei Du
- grid.13291.380000 0001 0807 1581West China Biomedical Big Data Center, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China ,grid.16890.360000 0004 1764 6123Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
| | - Xixi Feng
- grid.413856.d0000 0004 1799 3643School of Public Health, Chengdu Medical College, Chengdu, China
| | - Daihai He
- grid.16890.360000 0004 1764 6123Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China
| | - Yaolong Chen
- grid.32566.340000 0000 8571 0482Institute of Health Data Science, Lanzhou University, Lanzhou, China
| | - Linda L. D. Zhong
- grid.59025.3b0000 0001 2224 0361Biomedical Sciences and Chinese Medicine, School of Biological Sciences, Nanyang Technological University, Singapore, Singapore ,grid.221309.b0000 0004 1764 5980School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, China
| | - Xiaodong Yan
- School of Economics, Shandong University, Jinan, China.
| | - Jiawei Luo
- West China Biomedical Big Data Center, West China Hospital/West China School of Medicine, Sichuan University, Chengdu, China.
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35
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Abate D, Tesfaye H, Kassaw M, Addis Y, Mossie H. Impact of adopting the climate-smart crop varieties on food security in southwestern Ethiopia. AFRICAN JOURNAL OF SCIENCE, TECHNOLOGY, INNOVATION AND DEVELOPMENT 2023. [DOI: 10.1080/20421338.2022.2157787] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Affiliation(s)
- Dubale Abate
- Department of Agribusiness and Value Chain Management, Injibara University, Ethiopia
| | - Henoke Tesfaye
- Department of Agribusiness and Value Chain Management, Wolkite University, Ethiopia
| | - Metadel Kassaw
- Department of Agribusiness and Value Chain Management, Wolkite University, Ethiopia
| | - Yonnas Addis
- Department of Agribusiness and Value Chain Management, Wolkite University, Ethiopia
| | - Habtamu Mossie
- Department of Agricultural Economics, Injibara University, Ethiopia
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36
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Shu X, Ye Y. Knowledge Discovery: Methods from data mining and machine learning. SOCIAL SCIENCE RESEARCH 2023; 110:102817. [PMID: 36796993 DOI: 10.1016/j.ssresearch.2022.102817] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Revised: 10/17/2022] [Accepted: 10/18/2022] [Indexed: 06/18/2023]
Abstract
The interdisciplinary field of knowledge discovery and data mining emerged from a necessity of big data requiring new analytical methods beyond the traditional statistical approaches to discover new knowledge from the data mine. This emergent approach is a dialectic research process that is both deductive and inductive. The data mining approach automatically or semi-automatically considers a larger number of joint, interactive, and independent predictors to address causal heterogeneity and improve prediction. Instead of challenging the conventional model-building approach, it plays an important complementary role in improving model goodness of fit, revealing valid and significant hidden patterns in data, identifying nonlinear and non-additive effects, providing insights into data developments, methods, and theory, and enriching scientific discovery. Machine learning builds models and algorithms by learning and improving from data when the explicit model structure is unclear and algorithms with good performance are difficult to attain. The most recent development is to incorporate this new paradigm of predictive modeling with the classical approach of parameter estimation regressions to produce improved models that combine explanation and prediction.
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Affiliation(s)
| | - Yiwan Ye
- University of California Davis, USA
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37
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Yi GY, Chen LP. Estimation of the average treatment effect with variable selection and measurement error simultaneously addressed for potential confounders. Stat Methods Med Res 2023; 32:691-711. [PMID: 36694932 PMCID: PMC10119903 DOI: 10.1177/09622802221146308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
In the framework of causal inference, the inverse probability weighting estimation method and its variants have been commonly employed to estimate the average treatment effect. Such methods, however, are challenged by the presence of irrelevant pre-treatment variables and measurement error. Ignoring these features and naively applying the usual inverse probability weighting estimation procedures may typically yield biased inference results. In this article, we develop an inference method for estimating the average treatment effect with those features taken into account. We establish theoretical properties for the resulting estimator and carry out numerical studies to assess the finite sample performance of the proposed estimator.
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Affiliation(s)
- Grace Y Yi
- Department of Statistical and Actuarial Sciences, 6221University of Western Ontario, London, Canada.,Department of Computer Science, 6221University of Western Ontario, London, Canada
| | - Li-Pang Chen
- Department of Statistical and Actuarial Sciences, 6221University of Western Ontario, London, Canada.,Department of Statistics, 34913National Chengchi University, Taipei, Taiwan
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38
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CART model to classify the drought status of diverse tomato genotypes by VPD, air temperature, and leaf-air temperature difference. Sci Rep 2023; 13:602. [PMID: 36635417 PMCID: PMC9837056 DOI: 10.1038/s41598-023-27798-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 01/09/2023] [Indexed: 01/14/2023] Open
Abstract
Regular water management is crucial for the cultivation of tomato (Solanum lycopersicum L.). Inadequate irrigation leads to water stress and a reduction in tomato yield and quality. Therefore, it is important to develop an efficient classification method of the drought status of tomato for the timely application of irrigation. In this study, a simple classification and regression tree (CART) model that includes air temperature, vapor pressure deficit, and leaf-air temperature difference was established to classify the drought status of three tomato genotypes (i.e., cherry type 'Tainan ASVEG No. 19', large fruits breeding line '108290', and wild accession 'LA2093'). The results indicate that the proposed CART model exhibited a higher predictive sensitivity, specificity, geometric mean, and accuracy performance compared to the logistic model. In addition, the CART model was applicable not only to three tomato genotypes but across vegetative and reproductive stages. Furthermore, while the drought status was divided into low, medium, and high, the CART model provided a higher predictive performance than that of the logistic model. The results suggest that the drought status of tomato can be accurately classified by the proposed CART model. These results will provide a useful tool of the regular water management for tomato cultivation.
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39
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Hu A. Heterogeneous treatment effects analysis for social scientists: A review. SOCIAL SCIENCE RESEARCH 2023; 109:102810. [PMID: 36470639 DOI: 10.1016/j.ssresearch.2022.102810] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2021] [Revised: 10/07/2022] [Accepted: 10/11/2022] [Indexed: 06/17/2023]
Abstract
Social scientists have long been interested in the varying responses to a specific intervention, motivating the enterprise of heterogeneous treatment effects (HTE) analysis. Over the past five decades, the rapid development of HTE methods, from conventional multiplicative interactions in linear models to explorations based on machine learning techniques, has been witnessed. This article presents a systematic review of major HTE methods, including multiplicative interaction modeling, generalized additive modeling, propensity-score-based methods, marginal treatment effect, separate LASSO constraints, causal trees, causal forests, Bayesian additive regression trees, and meta-learners (i.e., the S-learner, T-learner, X-learner, and R-learner). These methods, as described roughly in a chronological order to emphasize methodological developments, are addressed to highlight their respective strengths and limitations. Following an illustrative example, this article reflects on future methodological developments.
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Affiliation(s)
- Anning Hu
- Professor of Sociology, Department of Sociology, Yale-Fudan Center for Cultural Sociology, Fudan University, China.
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40
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Zhou Y, Dowsey M, Spelman T, Choong P, Schilling C. SMART choice (knee) tool: a patient-focused predictive model to predict improvement in health-related quality of life after total knee arthroplasty. ANZ J Surg 2023; 93:316-327. [PMID: 36637215 DOI: 10.1111/ans.18250] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 12/11/2022] [Accepted: 12/21/2022] [Indexed: 01/14/2023]
Abstract
BACKGROUND Current predictive tools for TKA focus on clinicians rather than patients as the intended user. The purpose of this study was to develop a patient-focused model to predict health-related quality of life outcomes at 1-year post-TKA. METHODS Patients who underwent primary TKA for osteoarthritis from a tertiary institutional registry after January 2006 were analysed. The primary outcome was improvement after TKA defined by the minimal clinically important difference in utility score at 1-year post-surgery. Potential predictors included demographic information, comorbidities, lifestyle factors, and patient-reported outcome measures. Four models were developed, including both conventional statistics and machine learning (artificial intelligence) methods: logistic regression, classification tree, extreme gradient boosted trees, and random forest models. Models were evaluated using discrimination and calibration metrics. RESULTS A total of 3755 patients were included in the study. The logistic regression model performed the best with respect to both discrimination (AUC = 0.712) and calibration (intercept = -0.083, slope = 1.123, Brier score = 0.202). Less than 2% (n = 52) of the data were missing and therefore removed for complete case analysis. The final model used age (categorical), sex, baseline utility score, and baseline Veterans-RAND 12 responses as predictors. CONCLUSION The logistic regression model performed better than machine learning algorithms with respect to AUC and calibration plot. The logistic regression model was well calibrated enough to stratify patients into risk deciles based on their likelihood of improvement after surgery. Further research is required to evaluate the performance of predictive tools through pragmatic clinical trials. LEVEL OF EVIDENCE Level II, decision analysis.
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Affiliation(s)
- Yushy Zhou
- Department of Surgery, The University of Melbourne, Melbourne, Victoria, Australia
| | - Michelle Dowsey
- Department of Surgery, The University of Melbourne, Melbourne, Victoria, Australia
| | - Tim Spelman
- Department of Surgery, The University of Melbourne, Melbourne, Victoria, Australia
| | - Peter Choong
- Department of Surgery, The University of Melbourne, Melbourne, Victoria, Australia
| | - Chris Schilling
- Department of Surgery, The University of Melbourne, Melbourne, Victoria, Australia
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Gur Ali O. Targeting resources efficiently and justifiably by combining causal machine learning and theory. Front Artif Intell 2022; 5:1015604. [PMID: 36568581 PMCID: PMC9768181 DOI: 10.3389/frai.2022.1015604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 11/09/2022] [Indexed: 12/12/2022] Open
Abstract
Introduction Efficient allocation of limited resources relies on accurate estimates of potential incremental benefits for each candidate. These heterogeneous treatment effects (HTE) can be estimated with properly specified theory-driven models and observational data that contain all confounders. Using causal machine learning to estimate HTE from big data offers higher benefits with limited resources by identifying additional heterogeneity dimensions and fitting arbitrary functional forms and interactions, but decisions based on black-box models are not justifiable. Methods Our solution is designed to increase resource allocation efficiency, enhance the understanding of the treatment effects, and increase the acceptance of the resulting decisions with a rationale that is in line with existing theory. The case study identifies the right individuals to incentivize for increasing their physical activity to maximize the population's health benefits due to reduced diabetes and heart disease prevalence. We leverage large-scale data from multi-wave nationally representative health surveys and theory from the published global meta-analysis results. We train causal machine learning ensembles, extract the heterogeneity dimensions of the treatment effect, sign, and monotonicity of its moderators with explainable AI, and incorporate them into the theory-driven model with our generalized linear model with the qualitative constraint (GLM_QC) method. Results The results show that the proposed methodology improves the expected health benefits for diabetes by 11% and for heart disease by 9% compared to the traditional approach of using the model specification from the literature and estimating the model with large-scale data. Qualitative constraints not only prevent counter-intuitive effects but also improve achieved benefits by regularizing the model.
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Affiliation(s)
- Ozden Gur Ali
- College of Administrative Sciences and Economics, Koç University, Istanbul, Turkey
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42
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Maciel FA, Duarte D. The Impact of Cash Transfer Participation on Unhealthy Consumption in Brazil. HEALTH POLICY OPEN 2022. [DOI: 10.1016/j.hpopen.2022.100087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
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Li H, Tang H, Zhou W, Wan X. Impact of enterprise digitalization on green innovation performance under the perspective of production and operation. Front Public Health 2022; 10:971971. [PMID: 36466530 PMCID: PMC9714327 DOI: 10.3389/fpubh.2022.971971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 09/12/2022] [Indexed: 11/18/2022] Open
Abstract
Introduction How enterprises should practice digitalization transformation to effectively improve green innovation performance is related to the sustainable development of enterprises and the economy, which is an important issue that needs to be clarified. Methods This research uses the perspective of production and operation to deconstruct the digitalization of industrial listed enterprises from 2016 to 2020 into six features. A variety of machine learning methods are used, including DBSCAN, CART and other algorithms, to specifically explore the complex impact of enterprise digitalization feature configuration on green innovation performance. Conclusions (1) The more advanced digitalization transformation the enterprises have, the more possibly the high green innovation performance can be achieved. (2) Digitalization innovation is the digitalization element with the strongest influence ability on green innovation performance. (3) As the advancement of digitalization transformation, enterprises should also focus on digitalization innovation input and digitalization operation output, otherwise they should pay attention to digitalization management and digitalization operation output. Discussion The conclusions of this research will help enterprises understand their digitalization competitiveness and how to practice digitalization transformation to enhance green innovation performance, and also help the government to formulate policies to promote the development of green innovation in the digital economy era.
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Affiliation(s)
- Hailin Li
- College of Business Administration, Huaqiao University, Quanzhou, China,Research Center for Applied Statistics and Big Data, Huaqiao University, Xiamen, China
| | - Hongqin Tang
- Research Center for Applied Statistics and Big Data, Huaqiao University, Xiamen, China
| | - Wenhao Zhou
- College of Business Administration, Huaqiao University, Quanzhou, China
| | - Xiaoji Wan
- College of Business Administration, Huaqiao University, Quanzhou, China,*Correspondence: Xiaoji Wan
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Hashimoto Y, Yasunaga H. Theory and practice of propensity score analysis. ANNALS OF CLINICAL EPIDEMIOLOGY 2022; 4:101-109. [PMID: 38505253 PMCID: PMC10760486 DOI: 10.37737/ace.22013] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 03/21/2024]
Abstract
Propensity score analysis has been widely used in observational studies to make a causal inference. This study introduces three assumptions for causal inferences-conditional exchangeability, positivity, and consistency-and five steps for propensity score (PS) analysis-1) construct appropriate PS models, 2) check overlap in PS, 3) apply appropriate weighting (inverse probability of treatment weighting, standardized mortality ratio weighting, matching weights, and overlap weights) or matching methods according to the target of inference, 4) check the balance of covariates, and 5) estimate the effect of exposure appropriately. Finally, the advantages of PS analyses over conventional multivariable regression are discussed.
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Affiliation(s)
- Yohei Hashimoto
- Department of Clinical Epidemiology and Health Economics, School of Public Health, The University of Tokyo
- Department of Ophthalmology, Graduate School of Medicine, The University of Tokyo
| | - Hideo Yasunaga
- Department of Clinical Epidemiology and Health Economics, School of Public Health, The University of Tokyo
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Machine learning nonresponse adjustment of patient-reported opioid consumption data to enable consumption-informed postoperative opioid prescribing guidelines. SURGERY IN PRACTICE AND SCIENCE 2022; 10. [DOI: 10.1016/j.sipas.2022.100098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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46
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Liu Z, Zheng J, Tu C, Pan Y. Estimation for volunteer web survey samples using a model-averaging approach. J Appl Stat 2022; 50:3251-3271. [PMID: 37969891 PMCID: PMC10637194 DOI: 10.1080/02664763.2022.2107187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Accepted: 07/24/2022] [Indexed: 10/16/2022]
Abstract
Propensity score approach is a popular technique for estimating the population based on volunteer web survey samples. Various models have been used to estimate propensity scores and produce different population estimates. To obtain more accurate population estimators, we propose a model-averaging estimation approach based on propensity score estimates from a parametric logistic regression model and a nonparametric generalized boosted model. Consistency and asymptotic normality of the proposed estimators are established. A computation algorithm is also developed to implement the proposed method. Simulation studies are conducted to compare the performance of the proposed method with the other methods. A survey data from the Netizen Social Awareness Survey (NSAS) is used to illustrate the proposed methodology.
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Affiliation(s)
- Zhan Liu
- Hubei Key Laboratory of Applied Mathematics, School of Mathematics and Statistics, Hubei University, Wuhan, People's Republic of China
| | - Junbo Zheng
- Hubei Key Laboratory of Applied Mathematics, School of Mathematics and Statistics, Hubei University, Wuhan, People's Republic of China
| | - Chaofeng Tu
- Hubei Key Laboratory of Applied Mathematics, School of Mathematics and Statistics, Hubei University, Wuhan, People's Republic of China
| | - Yingli Pan
- Hubei Key Laboratory of Applied Mathematics, School of Mathematics and Statistics, Hubei University, Wuhan, People's Republic of China
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Metabolite, protein, and tissue dysfunction associated with COVID-19 disease severity. Sci Rep 2022; 12:12204. [PMID: 35842456 PMCID: PMC9288092 DOI: 10.1038/s41598-022-16396-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 07/08/2022] [Indexed: 01/09/2023] Open
Abstract
Proteins are direct products of the genome and metabolites are functional products of interactions between the host and other factors such as environment, disease state, clinical information, etc. Omics data, including proteins and metabolites, are useful in characterizing biological processes underlying COVID-19 along with patient data and clinical information, yet few methods are available to effectively analyze such diverse and unstructured data. Using an integrated approach that combines proteomics and metabolomics data, we investigated the changes in metabolites and proteins in relation to patient characteristics (e.g., age, gender, and health outcome) and clinical information (e.g., metabolic panel and complete blood count test results). We found significant enrichment of biological indicators of lung, liver, and gastrointestinal dysfunction associated with disease severity using publicly available metabolite and protein profiles. Our analyses specifically identified enriched proteins that play a critical role in responses to injury or infection within these anatomical sites, but may contribute to excessive systemic inflammation within the context of COVID-19. Furthermore, we have used this information in conjunction with machine learning algorithms to predict the health status of patients presenting symptoms of COVID-19. This work provides a roadmap for understanding the biochemical pathways and molecular mechanisms that drive disease severity, progression, and treatment of COVID-19.
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Padula WV, Kreif N, Vanness DJ, Adamson B, Rueda JD, Felizzi F, Jonsson P, IJzerman MJ, Butte A, Crown W. Machine Learning Methods in Health Economics and Outcomes Research-The PALISADE Checklist: A Good Practices Report of an ISPOR Task Force. VALUE IN HEALTH : THE JOURNAL OF THE INTERNATIONAL SOCIETY FOR PHARMACOECONOMICS AND OUTCOMES RESEARCH 2022; 25:1063-1080. [PMID: 35779937 DOI: 10.1016/j.jval.2022.03.022] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 03/25/2022] [Indexed: 06/15/2023]
Abstract
Advances in machine learning (ML) and artificial intelligence offer tremendous potential benefits to patients. Predictive analytics using ML are already widely used in healthcare operations and care delivery, but how can ML be used for health economics and outcomes research (HEOR)? To answer this question, ISPOR established an emerging good practices task force for the application of ML in HEOR. The task force identified 5 methodological areas where ML could enhance HEOR: (1) cohort selection, identifying samples with greater specificity with respect to inclusion criteria; (2) identification of independent predictors and covariates of health outcomes; (3) predictive analytics of health outcomes, including those that are high cost or life threatening; (4) causal inference through methods, such as targeted maximum likelihood estimation or double-debiased estimation-helping to produce reliable evidence more quickly; and (5) application of ML to the development of economic models to reduce structural, parameter, and sampling uncertainty in cost-effectiveness analysis. Overall, ML facilitates HEOR through the meaningful and efficient analysis of big data. Nevertheless, a lack of transparency on how ML methods deliver solutions to feature selection and predictive analytics, especially in unsupervised circumstances, increases risk to providers and other decision makers in using ML results. To examine whether ML offers a useful and transparent solution to healthcare analytics, the task force developed the PALISADE Checklist. It is a guide for balancing the many potential applications of ML with the need for transparency in methods development and findings.
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Affiliation(s)
- William V Padula
- Department of Pharmaceutical and Health Economics, School of Pharmacy, University of Southern California, Los Angeles, CA, USA; The Leonard D. Schaeffer Center for Health Policy & Economics, University of Southern California, Los Angeles, CA, USA.
| | - Noemi Kreif
- Centre for Health Economics, University of York, York, England, UK
| | - David J Vanness
- Department of Health Policy and Administration, College of Health and Human Development, Pennsylvania State University, Hershey, PA, USA
| | | | | | | | - Pall Jonsson
- National Institute for Health and Care Excellence, Manchester, England, UK
| | - Maarten J IJzerman
- Centre for Health Policy, School of Population and Global Health, University of Melbourne, Melbourne, Australia
| | - Atul Butte
- School of Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - William Crown
- The Heller School for Social Policy and Management, Brandeis University, Waltham, MA, USA.
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Jiao B, Guo Y, Gong D, Chen Q. Dynamic Ensemble Selection for Imbalanced Data Streams With Concept Drift. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; PP:1278-1291. [PMID: 35731763 DOI: 10.1109/tnnls.2022.3183120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Ensemble learning, as a popular method to tackle concept drift in data stream, forms a combination of base classifiers according to their global performances. However, concept drift generally occurs in local data space, causing significantly different performances of a base classifier at different locations. Thus, employing global performance as a criterion to select base classifier is inappropriate. Moreover, data stream is often accompanied by class imbalance problem, which affects the classification accuracy of ensemble learning on minority instances. To drawback these problems, a dynamic ensemble selection for imbalanced data streams with concept drift (DES-ICD) is proposed. For data arrived in chunk-by-chunk, a novel synthetic minority oversampling technique with adaptive nearest neighbors (AnnSMOTE) is developed to generate new minority instances that conform to the new concept. Following that, DES-ICD creates a base classifier on newly arrived data chunk balanced by AnnSMOTE and merges it with historical base classifiers to form a candidate classifier pool. For each query instance, the optimal combination is constructed in terms of the performance of candidate classifiers in its neighborhood. Experimental results for nine synthetic and five real-world datasets show that the proposed method outperforms seven comparative methods on classification accuracy and tracks new concepts in an imbalanced data stream more preciously.
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50
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Wyss R, Yanover C, El-Hay T, Bennett D, Platt RW, Zullo AR, Sari G, Wen X, Ye Y, Yuan H, Gokhale M, Patorno E, Lin KJ. Machine learning for improving high-dimensional proxy confounder adjustment in healthcare database studies: an overview of the current literature. Pharmacoepidemiol Drug Saf 2022; 31:932-943. [PMID: 35729705 PMCID: PMC9541861 DOI: 10.1002/pds.5500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 06/01/2022] [Accepted: 06/05/2022] [Indexed: 11/10/2022]
Abstract
Controlling for large numbers of variables that collectively serve as 'proxies' for unmeasured factors can often improve confounding control in pharmacoepidemiologic studies utilizing administrative healthcare databases. There is a growing body of evidence showing that data-driven machine learning algorithms for high-dimensional proxy confounder adjustment can supplement investigator-specified variables to improve confounding control compared to adjustment based on investigator-specified variables alone. Consequently, there has been a recent focus on the development of data-driven methods for high-dimensional proxy confounder adjustment. In this paper, we discuss the considerations underpinning three areas for data-driven high-dimensional proxy confounder adjustment: 1) feature generation-transforming raw data into covariates (or features) to be used for proxy adjustment; 2) covariate prioritization, selection and adjustment; and 3) diagnostic assessment. We survey current approaches and recent advancements within each area, including the most widely used approach to proxy confounder adjustment in healthcare database studies (the high-dimensional propensity score or hdPS). We also discuss limitations of the hdPS and outline recent advancements that incorporate the principles of proxy adjustment with machine learning extensions to improve performance. We further discuss challenges and avenues of future development within each area. This manuscript is endorsed by the International Society for Pharmacoepidemiology (ISPE). This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Richard Wyss
- Division of Pharmacoepidemioogy and Pharmacoeconomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Tal El-Hay
- KI Research Institute, Kfar Malal, Israel.,IBM Research-Haifa Labs, Haifa, Israel
| | - Dimitri Bennett
- Global Evidence and Outcomes, Takeda Pharmaceutical Company Ltd., Cambridge, MA, USA
| | | | - Andrew R Zullo
- Department of Health Services, Policy, and Practice, Brown University School of Public Health and Center of Innovation in Long-Term Services and Supports, Providence Veterans Affairs Medical Center, Providence, RI, USA
| | - Grammati Sari
- Real World Evidence Strategy Lead, Visible Analytics Ltd, Oxford, UK
| | - Xuerong Wen
- Health Outcomes, Pharmacy Practice, College of Pharmacy, University of Rhode Island, Kingston, RI, USA
| | - Yizhou Ye
- Global Epidemiology, AbbVie Inc. North Chicago, IL, USA
| | - Hongbo Yuan
- Canadian Agency for Drugs and Technologies in Health, Ottawa, Canada
| | - Mugdha Gokhale
- Pharmacoepidemiology, Center for Observational and Real-world Evidence, Merck, PA, USA
| | - Elisabetta Patorno
- Division of Pharmacoepidemioogy and Pharmacoeconomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kueiyu Joshua Lin
- Division of Pharmacoepidemioogy and Pharmacoeconomics, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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