Survival-Convolution Models for Predicting COVID-19 Cases and Assessing Effects of Mitigation Strategies

Countries around the globe have implemented unprecedented measures to mitigate the coronavirus disease 2019 (COVID-19) pandemic. We aim to predict COVID-19 disease course and compare effectiveness of mitigation measures across countries to inform policy decision making. We propose a robust and parsimonious survival-convolution model for predicting key statistics of COVID-19 epidemics (daily new cases). We account for transmission during a pre-symptomatic incubation period and use a time-varying effective reproduction number (R_t) to reflect the temporal trend of transmission and change in response to a public health intervention. We estimate the intervention effect on reducing the infection rate and quantify uncertainty by permutation. In China and South Korea, we predicted the entire disease epidemic using only data in the early phase (two to three weeks after the outbreak). A fast rate of decline in R_t was observed and adopting mitigation strategies early in the epidemic was effective in reducing the infection rate in these two countries. The lockdown in Italy did not further accelerate the speed at which the infection rate decreases. The effective reproduction number has staggered around R_t = 1.0 for more than 2 weeks before decreasing to below 1.0, and the epidemic in Italy is currently under control. In the US, R_t significantly decreased during a 2-week period after the declaration of national emergency, but afterwards the rate of decrease is substantially slower. If the trend continues after May 1, the first wave of COVID-19 may be controlled by July 26 (CI: July 9 to August 27). However, a loss of temporal effect on infection rate (e.g., due to relaxing mitigation measures after May 1) could lead to a long delay in controlling the epidemic (November 19 with less than 100 daily cases) and a total of more than 2 million cases.

Diabetes, Cognitive Decline, and Mild Cognitive Impairment Among Diverse Hispanics/Latinos: Study of Latinos-Investigation of Neurocognitive Aging Results (HCHS/SOL)

OBJECTIVE

Hispanics/Latinos are the largest ethnic/racial group in the U.S., have the highest prevalence of diabetes, and are at increased risk for neurodegenerative disorders. Currently, little is known about the relationship between diabetes and cognitive decline and disorders among diverse Hispanics/Latinos. The purpose of this study is to clarify these relationships in diverse middle-aged and older Hispanics/Latinos.

RESEARCH DESIGN AND METHODS

The Study of Latinos-Investigation of Neurocognitive Aging (SOL-INCA) is an ancillary study of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). HCHS/SOL is a multisite (Bronx, NY; Chicago, IL; Miami, FL; and San Diego, CA), probability-sampled (i.e., representative of targeted populations), and prospective cohort study. Between 2016 and 2018, SOL-INCA enrolled diverse Hispanics/Latinos aged ≥50 years (n = 6,377). Global cognitive decline and mild cognitive impairment (MCI) were the primary outcomes.

RESULTS

Prevalent diabetes at visit 1, but not incident diabetes at visit 2, was associated with significantly steeper global cognitive decline (β_GC = -0.16 [95% CI -0.25; -0.07]; P < 0.001), domain-specific cognitive decline, and higher odds of MCI (odds ratio 1.74 [95% CI 1.34; 2.26]; P < 0.001) compared with no diabetes in age- and sex-adjusted models.

CONCLUSIONS

Diabetes was associated with cognitive decline and increased MCI prevalence among diverse Hispanics/Latinos, primarily among those with prevalent diabetes at visit 1. Our findings suggest that significant cognitive decline and MCI may be considered additional disease complications of diabetes among diverse middle-aged and older Hispanics/Latinos.

Association of HSD17B13 rs72613567:TA with non-alcoholic fatty liver disease in Hispanics/Latinos

INTRODUCTION

Non-alcoholic fatty liver disease (NAFLD) disproportionately affects Hispanic/Latinos and rates of NAFLD vary among Hispanics from different background groups. Genetic variants and continental ancestry contribute to NAFLD disparities among Hispanics. We evaluated two newly identified NAFLD-associated single nucleotide polymorphisms of HSD17B13, rs72613567:TA and rs62305723:A in Hispanics/Latinos.

METHODS

Clinical data, genotypes of variants of interest and estimates of continental ancestry were extracted from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) database, which includes a cohort of 16 415 US Hispanic/Latinos. Surrogate endpoints for NAFLD were suspected NAFLD based on unexplained aminotransferase elevation, continuous ALT levels and FIB-4 scores to estimate hepatic fibrosis.

RESULTS

In all, 9342 participants were included for analysis. The rs72613567:TA allele was found in 15.3% and the rs62305723:A allele was identified in 4.5% of HCHS/SOL participants. rs72613567:TA was less frequent in persons with vs without suspected NAFLD (12.4% vs 15.7%, P < .001) and rs72613567:TA was associated with lower FIB-4 scores (P = .01). For persons with the NAFLD-associated PNPLA3 rs738409:G allele, the presence of rs72613567:TA was associated with a lower rate of suspected NAFD (odds ratio = 0.76, P < .001). rs72613567:TA was less frequent in Hispanic/Latino background groups with higher rates of suspected NAFLD. The rs62305723:A allele was not associated with suspected NAFLD or FIB-4 score.

CONCLUSION

The rs72613567:TA allele is associated with lower rates of suspected NAFLD and lower FIB-4 scores among Hispanic/Latinos and with lower rates of suspected NAFLD in persons with the PNPLA3 rs738409:G allele. The rs72613567:TA allele contributes to NAFLD disparities among Hispanic/Latino background groups.

Leisure-time physical activity volume, intensity, and duration from mid- to late-life in U.S. subpopulations by race and sex. The Atherosclerosis Risk In Communities (ARIC) Study

Mitigating age-related disease and disability presents challenges. Physical activity (PA) may be influential for prolonging health and functioning, warranting characterization of its patterns over the life course in population-based data. With the availability of up to three self-reported assessments of past year leisure-time PA (LTPA) over multiple decades in 15,036 participants (26% African American; 55% women; mean baseline age=54; median follow-up=23 years) from the Atherosclerosis Risk in Communities (ARIC) Study sampled from four U.S. communities, race-sex-stratified trajectories of average weekly intensity (metabolic equivalent of task (MET)), duration (hours), and energy expenditure or volume (MET-h) of LTPA were developed from age 45 to 90 using joint models to accommodate expected non-ignorable attrition. Declines in weekly LTPA intensity, duration, and volume from age 70 to 90 were observed in white women (2.9 to 1.2 MET; 2.5 to 0.6 h; 11.1 to 2.6 MET-h), white men (2.5 to 1.0 MET; 3.5 to 1.8 h; 15.5 to 6.4 MET-h), African American women (2.5 to 2.4 MET; 0.8 to 0.1 h; 6.7 to 6.0 MET-h), and African American men (2.3 to 1.4 MET; 1.5 to 0.6 h; 8.0 to 2.3 MET-h). These data reveal population-wide shifts towards less active lifestyles in older adulthood.

Outcome prediction models in AQP4-IgG positive neuromyelitis optica spectrum disorders

Pathogenic antibodies targeting the aquaporin-4 water channel on astrocytes are associated with relapsing inflammatory neuromyelitis optica spectrum disorders. The clinical phenotype is characterized by recurrent episodes of optic neuritis, longitudinally extensive transverse myelitis, area postrema attacks and less common brainstem and cerebral events. Patients often develop major residual disability from these attacks, so early diagnosis and initiation of attackpreventing medications is important. Accurate prediction of relapse would assist physicians in counselling patients, planning treatment and designing clinical trials. We used a large multicentre dataset of 441 patients from the UK, USA, Japan and Martinique who collectively experienced 1976 attacks, and applied sophisticated mathematical modelling to predict likelihood of relapse and disability at different time points. We found that Japanese patients had a lower risk of subsequent attacks except for brainstem and cerebral events, with an overall relative relapse risk of 0.681 (P = 0.001) compared to Caucasians and African patients, who had a higher likelihood of cerebral attacks, with a relative relapse risk of 3.309 (P = 0.009) compared to Caucasians. Female patients had a higher chance of relapse than male patients (P = 0.009), and patients with younger age of onset were more likely to have optic neuritis relapses (P < 0.001). Immunosuppressant drugs reduced and multiple sclerosis disease-modifying agents increased the likelihood of relapse (P < 0.001). Patients with optic neuritis at onset were more likely to develop blindness (P < 0.001), and those with older age of onset were more likely to develop ambulatory disability. Only 25% of long-term disability was related to initial onset attack, indicating the importance of early attack prevention. With respect to selection of patients for clinical trial design, there would be no gain in power by selecting recent onset patients and only a small gain by selecting patients with recent high disease activity. We provide risk estimates of relapse and disability for patients diagnosed and treated with immunosuppressive treatments over the subsequent 2, 3, 5 and 10 years according to type of attack at onset or the first 2-year course, ethnicity, sex and onset age. This study supports significant effects of onset age, onset phenotype and ethnicity on neuromyelitis optica spectrum disorders outcomes. Our results suggest that powering clinical treatment trials based upon relapse activity in the preceding 2 years may offer little benefit in the way of attack risk yet severely hamper clinical trial success.

Prescription Opioid Use Among Hispanics/Latinos With Arthritis Symptoms: Results From the Hispanic Community Health Study/Study of Latinos

INTRODUCTION

To determine the prevalence of prescription opioid (PO) use among Hispanics/Latinos with arthritis symptoms and to characterize how demographic and cultural factors are associated with PO use.

METHOD

Cross-sectional analysis of baseline visit data during 2008 to 2011 from the Hispanic Community Health Study/Study of Latinos, a population-based cohort study of 16,415 Hispanics/Latinos living in Chicago, Illinois, Miami, Florida, Bronx, New York, and San Diego, California. Included participants self-reported painful inflammation or swelling in one or more joints. Multivariate models controlling for physical and mental health scores were constructed to assess how demographic and cultural factors were associated with PO use.

RESULTS

A total of 9.3% were using POs at the time of the baseline visit. In multivariate models, persons of Cuban background (adjusted odds ratio [AOR] = 0.42, 95% confidence interval [CI; 0.21, 0.81]) and of Dominican background (AOR = 0.38, 95% CI [0.18, 0.80]) were significantly less likely to use POs compared with a reference group of persons of Mexican background. Greater language acculturation was also negatively associated with PO use (AOR = 0.68, 95% CI [0.53, 0.87]).

CONCLUSION

POs were used relatively uncommonly, and use showed marked variation between Hispanic/Latino groups. Future study should determine mechanisms for why greater use of English among Hispanics/Latinos might influence PO use.

A super-combo-drug test to detect adverse drug events and drug interactions from electronic health records in the era of polypharmacy

Pharmacoinformatics research has experienced a great deal of successes in detecting drug-induced adverse events (AEs) using large-scale health record databases. In the era of polypharmacy, pharmacoinformatics faces many new challenges, and two significant challenges are to detect high-order drug interactions and to handle strongly correlated drugs. In this article, we propose a super-combo-drug test (SupCD-T) to address the aforementioned two challenges. SupCD-T detects drug interactions by identifying optimal drug combinations with increased AE risks. In addition, SupCD-T increases the statistical powers to detect single-drug effects by combining strongly correlated drugs. Although SupCD-T does not distinguish single-drug effects from their combination effects, it is noticeably more powerful in selecting an individual drug effect in the multiple regression analysis, where confounding justification between two correlated drugs reduces the power in testing the individual drug effects on AEs. Our simulation studies demonstrate that SupCD-T has generally better power comparing with the multiple regression analysis. In addition, SupCD-T is able to select meaningful drug combinations (eg, highly coprescribed drugs). Using electronic health record database, we illustrate the utility of SupCD-T and discover a number of drug combinations that have increased risk in myopathy. Some novel drug combinations have not yet been investigated and reported in the pharmacology research.

Reducing the Population Burden of Coronary Heart Disease by Modifying Adiposity: Estimates From the ARIC Study

Background

Excess adiposity, which affects 69% of US adults, increases coronary heart disease (CHD) risk in an association that manifests below conventional obesity cut points. The population‐level impact on CHD risk that is attainable through modest adiposity reductions in populations is not well characterized. We estimated the effect of hypothetical reductions in both body mass index (BMI) and waist circumference (WC) on CHD incidence.

Methods and Results

The study population included 13 610 ARIC (Atherosclerosis Risk in Communities) participants. Our hypothetical reduction in BMI or WC was applied relative to the temporal trend, with no hypothetical reduction among those with BMI >24 or WC >88 cm, respectively. This threshold for hypothetical reduction is near the clinical guidelines for excess adiposity. CHD risk differences compared the hypothetical reduction with no reduction. Sensitivity analysis was conducted to estimate the effect of applying the hypothetical BMI reduction at the established overweight cut point of 25. Cumulative 12‐year CHD incidence with no intervention was 6.3% (95% CI, 5.9–6.8%). Risk differences following the hypothetical BMI and WC reductions were −0.6% (95% CI, −1.0% to −0.1%) and −1.0% (95% CI, −1.4% to −0.5%), respectively. These results were robust for the sensitivity analyses. Consequently, we estimated that this hypothetical reduction of 5% in BMI and WC, respectively, could have prevented 9% and 16%, respectively, of the CHD events occurring in this study population over 12 years, after adjustment for established CHD risk factors.

Conclusions

Meaningful CHD risk reductions could derive from modest reductions in adiposity attainable through lifestyle modification.

The AURORA Study: a longitudinal, multimodal library of brain biology and function after traumatic stress exposure

Adverse posttraumatic neuropsychiatric sequelae (APNS) are common among civilian trauma survivors and military veterans. These APNS, as traditionally classified, include posttraumatic stress, postconcussion syndrome, depression, and regional or widespread pain. Traditional classifications have come to hamper scientific progress because they artificially fragment APNS into siloed, syndromic diagnoses unmoored to discrete components of brain functioning and studied in isolation. These limitations in classification and ontology slow the discovery of pathophysiologic mechanisms, biobehavioral markers, risk prediction tools, and preventive/treatment interventions. Progress in overcoming these limitations has been challenging because such progress would require studies that both evaluate a broad spectrum of posttraumatic sequelae (to overcome fragmentation) and also perform in-depth biobehavioral evaluation (to index sequelae to domains of brain function). This article summarizes the methods of the Advancing Understanding of RecOvery afteR traumA (AURORA) Study. AURORA conducts a large-scale (n = 5000 target sample) in-depth assessment of APNS development using a state-of-the-art battery of self-report, neurocognitive, physiologic, digital phenotyping, psychophysical, neuroimaging, and genomic assessments, beginning in the early aftermath of trauma and continuing for 1 year. The goals of AURORA are to achieve improved phenotypes, prediction tools, and understanding of molecular mechanisms to inform the future development and testing of preventive and treatment interventions.

Association of insulin resistance, from mid-life to late-life, with aortic stiffness in late-life: the Atherosclerosis Risk in Communities Study

Background

Insulin resistance may contribute to aortic stiffening that leads to end-organ damage. We examined the cross-sectional association and prospective association of insulin resistance and aortic stiffness in older adults without diabetes.

Methods

We analyzed 2571 men and women at Visit 5 (in 2011–2013), and 2350 men and women at repeat examinations from baseline at Visit 1 (in 1987–1989) to Visit 5 (in 2011–2013). Linear regression was used to estimate the difference in aortic stiffness per standard unit of HOMA-IR, TG/HDL-C, and TyG at Visit 5. Linear mixed effects were used to assess if high, as opposed to non-high, aortic stiffness (> 75th percentile) was preceded by a faster annual rate of change in log-HOMA-IR, log-TG/HDL-C, and log-TyG from Visit 1 to Visit 5.

Results

The mean age of participants was 75 years, 37% (n = 957) were men, and 17% (n = 433) were African American. At Visit 5, higher HOMA-IR, higher TG/HDL-C, and higher TyG were associated with higher aortic stiffness (16 cm/s per SD (95% CI 6, 27), 29 cm/s per SD (95% CI 18, 40), and 32 cm/s per SD (95% CI 22, 42), respectively). From Visit 1 to Visit 5, high aortic stiffness, compared to non-high aortic stiffness, was not preceded by a faster annual rate of change in log-HOMA-IR from baseline to 9 years (0.030 (95% CI 0.024, 0.035) vs. 0.025 (95% CI 0.021, 0.028); p = 0.15) or 9 years onward (0.011 (95% CI 0.007, 0.015) vs. 0.011 (95% CI 0.009, 0.013); p = 0.31); in log-TG/HDL-C from baseline to 9 years (0.019 (95% CI 0.015, 0.024) vs. 0.024 (95% CI 0.022, 0.026); p = 0.06) or 9 years onward (− 0.007 (95% CI − 0.010, − 0.005) vs. − 0.009 (95% CI − 0.010, − 0.007); p = 0.08); or in log-TyG from baseline to 9 years (0.002 (95% CI 0.002, 0.003) vs. 0.003 (95% CI 0.003, 0.003); p = 0.03) or 9 years onward (0 (95% CI 0, 0) vs. 0 (95% CI 0, 0); p = 0.08).

Conclusions

Among older adults without diabetes, insulin resistance was associated with aortic stiffness, but the putative role of insulin resistance in aortic stiffness over the life course requires further study.

Sleep and neurocognitive decline in the Hispanic Community Health Study/Study of Latinos

INTRODUCTION

To determine if sleep-disordered breathing (SDB), daytime sleepiness, insomnia, and sleep duration predict seven-year neurocognitive decline in US Hispanics/Latinos (N = 5247).

METHODS

The exposures were baseline SDB, daytime sleepiness, insomnia, and sleep duration. The outcomes were change in episodic learning and memory (B-SEVLT-Sum and SEVLT-Recall), language (word fluency [WF]), processing speed (Digit Symbol Substitution), and a cognitive impairment screener (Six-item Screener [SIS]).

RESULTS

Mean age was 63 ± 8 years, with 55% of the population being female with 7.0% Central American, 24.5% Cuban, 9.3% Dominican, 35.9% Mexican, 14.4% Puerto Rican, and 5.1% South American background. Long sleep (>9 hours), but not short sleep (<6 hours), was associated with decline (standard deviation units) in episodic learning and memory (β_SEVLT-Sum ⁼ -0.22 [se = 0.06]; P < .001; β_SEVLT-Recall = -0.13 [se = 0.06]; P < .05), WF (Pwf = -0.20 [se 5 0.06]; P < .01), and SIS (β_SIS = -0.16 [se = 0.06]; P < .01), but not processing speed, after adjusting for covariates. SDB, sleepiness, and insomnia were not associated with neurocognitive decline.

CONCLUSION

Long sleep duration predicted seven-year cognitive decline.

Constructing disease onset signatures using multi-dimensional network-structured biomarkers

Potential disease-modifying therapies for neurodegenerative disorders need to be introduced prior to the symptomatic stage in order to be effective. However, current diagnosis of neurological disorders mostly rely on measurements of clinical symptoms and thus only identify symptomatic subjects in their late disease course. Thus, it is of interest to select and integrate biomarkers that may reflect early disease-related pathological changes for earlier diagnosis and recruiting pre-sypmtomatic subjects in a prevention clinical trial. Two sources of biological information are relevant to the construction of biomarker signatures for time to disease onset that is subject to right censoring. First, biomarkers' effects on disease onset may vary with a subject's baseline disease stage indicated by a particular marker. Second, biomarkers may be connected through networks, and their effects on disease may be informed by this network structure. To leverage these information, we propose a varying-coefficient hazards model to induce double smoothness over the dimension of the disease stage and over the space of network-structured biomarkers. The distinctive feature of the model is a non-parametric effect that captures non-linear change according to the disease stage and similarity among the effects of linked biomarkers. For estimation and feature selection, we use kernel smoothing of a regularized local partial likelihood and derive an efficient algorithm. Numeric simulations demonstrate significant improvements over existing methods in performance and computational efficiency. Finally, the methods are applied to our motivating study, a recently completed study of Huntington's disease (HD), where structural brain imaging measures are used to inform age-at-onset of HD and assist clinical trial design. The analysis offers new insights on the structural network signatures for premanifest HD subjects.

Entropy Learning for Dynamic Treatment Regimes

Estimating optimal individualized treatment rules (ITRs) in single or multi-stage clinical trials is one key solution to personalized medicine and has received more and more attention in statistical community. Recent development suggests that using machine learning approaches can significantly improve the estimation over model-based methods. However, proper inference for the estimated ITRs has not been well established in machine learning based approaches. In this paper, we propose a entropy learning approach to estimate the optimal individualized treatment rules (ITRs). We obtain the asymptotic distributions for the estimated rules so further provide valid inference. The proposed approach is demonstrated to perform well in finite sample through extensive simulation studies. Finally, we analyze data from a multi-stage clinical trial for depression patients. Our results offer novel findings that are otherwise not revealed with existing approaches.

Cognition and Daily Functioning: Results from the Hispanic Community Health Study/Study of Latinos (SOL) and Study of Latinos-Investigation of Neurocognitive Aging (SOL-INCA)

BACKGROUND

Among older adults, poorer cognitive functioning has been associated with impairments in instrumental activities of daily living (IADLs). However, IADL impairments among older Hispanics/Latinos is poorly understood.

OBJECTIVE

To characterize the relationships between cognition and risk for IADL impairment among diverse Hispanics/Latinos.

METHODS

Participants included 6,292 community-dwelling adults from the Study of Latinos - Investigation of Neurocognitive Aging, an ancillary study of 45+ year-olds in the Hispanic Community Health Study/Study of Latinos. Cognitive data (learning, memory, executive functioning, processing speed, and a Global cognitive composite) were collected at Visit 1. IADL functioning was self-reported 7 years later, and treated as a categorical (i.e., risk) and continuous (i.e., degree) measures of impairment. Survey two-part models (mixture of logit and generalized linear model with Gaussian distribution) and ordered logistic regression tested the associations of cognitive performance (individual tests and composite z-score) with IADL impairment. Additionally, we investigated the moderating role of age, sex, and Hispanic/Latino background on the association between cognition and IADL impairment.

RESULTS

Across all cognitive measures, poorer performance was associated with higher odds of IADL impairment 7 years later. Associations were generally stronger for the oldest group (70+ years) relative to the youngest group (50-59 years). Sex and Hispanic/Latino background did not modify the associations. Across the full sample, lower scores on learning, memory, and the Global cognitive composite were also associated with higher degree of IADL impairment.

CONCLUSION

Across diverse Hispanics/Latinos, cognitive health is an important predictor of everyday functioning 7 years later, especially in older adulthood.

Identifying disease-associated biomarker network features through conditional graphical model

Biomarkers are often organized into networks, in which the strengths of network connections vary across subjects depending on subject-specific covariates (eg, genetic variants). Variation of network connections, as subject-specific feature variables, has been found to predict disease clinical outcome. In this work, we develop a two-stage method to estimate biomarker networks that account for heterogeneity among subjects and evaluate network's association with disease clinical outcome. In the first stage, we propose a conditional Gaussian graphical model with mean and precision matrix depending on covariates to obtain covariate-dependent networks with connection strengths varying across subjects while assuming homogeneous network structure. In the second stage, we evaluate clinical utility of network measures (connection strengths) estimated from the first stage. The second-stage analysis provides the relative predictive power of between-region network measures on clinical impairment in the context of regional biomarkers and existing disease risk factors. We assess the performance of proposed method by extensive simulation studies and application to a Huntington's disease (HD) study to investigate the effect of HD causal gene on the rate of change in motor symptom through affecting brain subcortical and cortical gray matter atrophy connections. We show that cortical network connections and subcortical volumes, but not subcortical connections are identified to be predictive of clinical motor function deterioration. We validate these findings in an independent HD study. Lastly, highly similar patterns seen in the gray matter connections and a previous white matter connectivity study suggest a shared biological mechanism for HD and support the hypothesis that white matter loss is a direct result of neuronal loss as opposed to the loss of myelin or dysmyelination.

A research framework for cognitive aging and Alzheimer's disease among diverse US Latinos: Design and implementation of the Hispanic Community Health Study/Study of Latinos-Investigation of Neurocognitive Aging (SOL-INCA)

Hispanics/Latinos are the largest ethnic/racial group in the United States and at high risk for Alzheimer's disease and related dementia (ADRD). Yet, ADRD among diverse Latinos is poorly understood and disparately understudied or unstudied compared to other ethnic/racial groups that leave the nation ill-prepared for major demographic shifts that lay ahead in coming decades. The primary purpose of this Perspectives article was to provide a new research framework for advancing Latino ADRD knowledge, encompassing the unique sociocultural, cardiometabolic, and genomic aspects of Latino health, aging, and ADRD. In addition, we describe some of the research challenges to progress in Latino ADRD research. Finally, we present the Study of Latinos - Investigation of Neurocognitive Aging (SOL-INCA) as an example of implementing this new framework for advancing Latino ADRD research.

Prevalence and correlates of mild cognitive impairment among diverse Hispanics/Latinos: Study of Latinos-Investigation of Neurocognitive Aging results

INTRODUCTION

We estimated the prevalence and correlates of mild cognitive impairment (MCI) among middle-aged and older diverse Hispanics/Latinos.

METHODS

Middle-aged and older diverse Hispanics/Latinos enrolled (n = 6377; 50-86 years) in this multisite prospective cohort study were evaluated for MCI using the National Institute on Aging-Alzheimer's Association diagnostic criteria.

RESULTS

The overall MCI prevalence was 9.8%, which varied between Hispanic/Latino groups. Older age, high cardiovascular disease (CVD) risk, and elevated depressive symptoms were significant correlates of MCI prevalence. Apolipoprotein E4 (APOE) and APOE2 were not significantly associated with MCI.

DISCUSSION

MCI prevalence varied among Hispanic/Latino backgrounds, but not as widely as reported in the previous studies. CVD risk and depressive symptoms were associated with increased MCI, whereas APOE4 was not, suggesting alternative etiologies for MCI among diverse Hispanics/Latinos. Our findings suggest that mitigating CVD risk factors may offer important pathways to understanding and reducing MCI and possibly dementia among diverse Hispanics/Latinos.

Design and analysis of bridging studies with prior probabilities on the null and alternative hypotheses

The pharmaceutical industry and regulatory agencies are increasingly interested in conducting bridging studies in order to bring an approved drug product from the original region (eg, United States or European Union) to a new region (eg, Asian-Pacific countries). In this article, we provide a new methodology for the design and analysis of bridging studies by assuming prior knowledge on how the null and alternative hypotheses in the original, foreign study are related to the null and alternative hypotheses in the bridging study and setting the type I error for the bridging study according to the strength of the foreign-study evidence. The new methodology accounts for randomness in the foreign-study evidence and controls the average type I error of the bridging study over all possibilities of the foreign-study evidence. In addition, the new methodology increases statistical power, when compared to approaches that do not use foreign-study evidence, and it allows for the possibility of not conducting the bridging study when the foreign-study evidence is unfavorable. Finally, we conducted extensive simulation studies to demonstrate the usefulness of the proposed methodology.

Optimal Designs of Two-Phase Studies

The two-phase design is a cost-effective sampling strategy to evaluate the effects of covariates on an outcome when certain covariates are too expensive to be measured on all study subjects. Under such a design, the outcome and inexpensive covariates are measured on all subjects in the first phase and the first-phase information is used to select subjects for measurements of expensive covariates in the second phase. Previous research on two-phase studies has focused largely on the inference procedures rather than the design aspects. We investigate the design efficiency of the two-phase study, as measured by the semiparametric efficiency bound for estimating the regression coefficients of expensive covariates. We consider general two-phase studies, where the outcome variable can be continuous, discrete, or censored, and the second-phase sampling can depend on the first-phase data in any manner. We develop optimal or approximately optimal two-phase designs, which can be substantially more efficient than the existing designs. We demonstrate the improvements of the new designs over the existing ones through extensive simulation studies and two large medical studies.

The cross-sectional association of cognitive stimulation factors and cognitive function among Latino adults in Hispanic Community Health Study/Study of Latinos (HCHS/SOL)

INTRODUCTION

Higher cognitive stimulation (CS) is associated with improved cognition. Sources of CS among Hispanics/Latinos are understudied.

METHODS

In the Hispanic Community Health Study/Study of Latinos 2008 to 2011 (n = 9438), we used finite mixture models to generate latent CS profiles, and multivariate linear regressions to examine associations with cognition in Hispanic/Latino adults (45-74 years). CS included education, occupation, social network, and acculturation. Cognitive measures included the Six-Item Screener, Brief-Spanish English Verbal Learning Test Sum and Recall, Controlled Oral Word Association Test, Digit Symbol Substitution, and Global Cognition.

RESULTS

Two CS profiles emerged, and were labeled "typical" and "enhanced." The enhanced CS profile (22%) had more family connections, bicultural engagements, skilled/professional occupations, education, and higher cognitive scores.

DISCUSSION

An enhanced CS profile emerged from contextual and culturally relevant factors, and was associated with higher cognitive scores across all measures. This provides initial evidence on how factors coalesce to shape cognitive protection in Hispanics/Latinos.

Semiparametric frailty models for zero-inflated event count data in the presence of informative dropout

Recurrent events data are commonly encountered in medical studies. In many applications, only the number of events during the follow-up period rather than the recurrent event times is available. Two important challenges arise in such studies: (a) a substantial portion of subjects may not experience the event, and (b) we may not observe the event count for the entire study period due to informative dropout. To address the first challenge, we assume that underlying population consists of two subpopulations: a subpopulation nonsusceptible to the event of interest and a subpopulation susceptible to the event of interest. In the susceptible subpopulation, the event count is assumed to follow a Poisson distribution given the follow-up time and the subject-specific characteristics. We then introduce a frailty to account for informative dropout. The proposed semiparametric frailty models consist of three submodels: (a) a logistic regression model for the probability such that a subject belongs to the nonsusceptible subpopulation; (b) a nonhomogeneous Poisson process model with an unspecified baseline rate function; and (c) a Cox model for the informative dropout time. We develop likelihood-based estimation and inference procedures. The maximum likelihood estimators are shown to be consistent. Additionally, the proposed estimators of the finite-dimensional parameters are asymptotically normal and the covariance matrix attains the semiparametric efficiency bound. Simulation studies demonstrate that the proposed methodologies perform well in practical situations. We apply the proposed methods to a clinical trial on patients with myelodysplastic syndromes.

Over-expression of both VEGF-C and Twist predicts poor prognosis in human breast cancer

BACKGROUND

Angiogenesis is an indispensable step in the growth and invasiveness of breast cancers involving a series of exquisite molecular steps. Pro-angiogenic factors, including vascular endothelial growth factor (VEGF), have been recognized as pivotal therapeutic targets in the treatment of breast cancer. More recently, a highly conserved transcription factor Twist has been reported to be involved in tumor angiogenesis and metastasis.

METHODS

The expression of VEGF-C and Twist was immunohistochemically determined in tissue samples of primary tumors from 408 patients undergoing curative surgical resection for breast cancer. The correlations of VEGF-C and Twist expressions with clinicopathologic parameters as well as survival outcomes were evaluated.

RESULTS

Of the 408 patients evaluated, approximately 70% had high expression of VEGF-C which was significantly associated with advanced tumor stages (P = 0.019). Similarly, VEGF-C expression was associated with the proliferation index Ki67, N3 lymph node metastasis, and D2-40-positive lymphatic vessel invasion (LVI) in a univariate analysis. Furthermore, patients with high expressions of VEGF-C and Twist (V + T+) had significantly increased lymph node metastasis, higher clinical stage, and worse disease-free survival, DFS (P = 0.001) and overall survival, OS (P = 0.011).

CONCLUSIONS

Our results suggested that co-expression of VEGF-C and Twist was associated with larger tumor size, higher numbers of lymph node involvement, D2-40-positive LVI, higher risk of distant metastasis, and worse DFS or OS in breast cancer patients.

Controlling false discovery proportion in identification of drug-related adverse events from multiple system organ classes

Analyzing safety data from clinical trials to detect safety signals worth further examination involves testing multiple hypotheses, one for each observed adverse event (AE) type. There exists certain hierarchical structure for these hypotheses due to the classification of the AEs into system organ classes, and these AEs are also likely correlated. Many approaches have been proposed to identify safety signals under the multiple testing framework and tried to achieve control of false discovery rate (FDR). The FDR control concerns the expectation of the false discovery proportion (FDP). In practice, the control of the actual random variable FDP could be more relevant and has recently drawn much attention. In this paper, we proposed a two-stage procedure for safety signal detection with direct control of FDP, through a permutation-based approach for screening groups of AEs and a permutation-based approach of constructing simultaneous upper bounds for false discovery proportion. Our simulation studies showed that this new approach has controlled FDP. We demonstrate our approach using data sets derived from a drug clinical trial.

DASH diet and prevalent metabolic syndrome in the Hispanic Community Health Study/Study of Latinos

The Dietary Approaches to Stop Hypertension (DASH) diet is recommended for lowering blood pressure and preventing cardiovascular disease (CVD), but little data exist on these associations in US Hispanics/Latinos. We sought to assess associations between DASH score and prevalence of metabolic syndrome (MetS) and its components in diverse Hispanics/Latinos. We studied 10,741 adults aged 18–74 in the multicenter Hispanic Community Health Study/Study of Latinos. Dietary intake was measured using two 24-hour recalls, and MetS defined per the 2009 harmonized guidelines. We assessed cross-sectional associations of DASH score and MetS (and its dichotomized components) using survey logistic regression, and DASH and MetS continuous components using linear regression. We also stratified these models by Hispanic/Latino heritage group to explore heritage-specific associations. We found no associations between DASH and MetS prevalence. DASH was inversely associated with both measures of blood pressure (p < 0.01 for systolic and p < 0.001 for diastolic) in the overall cohort. DASH was also inversely associated with diastolic blood pressure in the Mexican (p < 0.05), Central American (p < 0.05), and South American (p < 0.01) groups; triglycerides (p < 0.05) in the Central American group; fasting glucose overall (p < 0.01) and in the Mexican group (p < 0.01); and waist circumference overall (p < 0.05) and in the South American group (p < 0.01). DASH was positively associated with HDL-cholesterol (p < 0.01) in the Central American group. DASH may better capture diet-MetS associations in Hispanic/Latino subpopulations such as Central/South Americans; this study also adds evidence that Hispanics/Latinos should be analyzed by heritage. Further research, and/or culturally tailored DASH measures will help further explain between-heritage differences.

Leveraging nonlinear dynamic models to predict progression of neuroimaging biomarkers

Using biomarkers to model disease course effectively and make early prediction is a challenging but critical path to improving diagnostic accuracy and designing preventive trials for neurological disorders. Leveraging the domain knowledge that certain neuroimaging biomarkers may reflect the disease pathology, we propose a model inspired by the neural mass model from cognitive neuroscience to jointly model nonlinear dynamic trajectories of the biomarkers. Under a nonlinear mixed-effects model framework, we introduce subject- and biomarker-specific random inflection points to characterize the critical time of underlying disease progression as reflected in the biomarkers. A latent liability score is shared across biomarkers to pool information. Our model allows assessing how the underlying disease progression will affect the trajectories of the biomarkers, and, thus, is potentially useful for individual disease management or preventive therapeutics. We propose an EM algorithm for maximum likelihood estimation, where in the E step, a normal approximation is used to facilitate numerical integration. We perform extensive simulation studies and apply the method to analyze data from a large multisite natural history study of Huntington's Disease (HD). The results show that some neuroimaging biomarker inflection points are early signs of the HD onset. Finally, we develop an online tool to provide the individual prediction of the biomarker trajectories given the medical history and baseline measurements.

EARLY DIAGNOSIS OF NEUROLOGICAL DISEASE USING PEAK DEGENERATION AGES OF MULTIPLE BIOMARKERS

Neurological diseases are due to the loss of structure or function of neurons that eventually leads to cognitive deficit, neuropsychiatric symptoms, and impaired activities of daily living. Identifying sensitive and specific biological and clinical markers for early diagnosis allows recruiting patients into a clinical trial to test therapeutic intervention. However, many biomarker studies considered a single biomarker at one time that fails to provide precise prediction for disease age at onset. In this paper, we use longitudinally collected measurements from multiple biomarkers and measurement error-corrected clinical diagnosis ages to identify which biomarkers and what features of biomarker trajectories are useful for early diagnosis. Specifically, we assume that the subject-specific biomarker trajectories depend on unobserved states of underlying latent variables with the conditional mean follows a nonlinear sigmoid shape. We show that peak degeneration age of the biomarker trajectory is useful for early diagnosis. We propose an Expectation-Maximization (EM) algorithm to obtain the maximum likelihood estimates of all parameters and conduct extensive simulation studies to examine the performance of the proposed methods. Finally, we apply our methods to studies of Alzheimer's disease and Huntington's disease and identify a few important biomarkers that can be used for early diagnosis.

Mendelian randomization of inorganic arsenic metabolism as a risk factor for hypertension- and diabetes-related traits among adults in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) cohort

BACKGROUND

Hypertension and diabetes have been associated with inefficient arsenic metabolism, primarily through studies undertaken in populations exposed through drinking water. Recently, rice has been recognized as a source of arsenic exposure, but it remains unclear whether populations with high rice consumption but no known water exposure are at risk for the health problems associated with inefficient arsenic metabolism.

METHODS

The relationships between arsenic metabolism efficiency (% inorganic arsenic, % monomethylarsenate and % dimethylarsinate in urine) and three hypertension- and seven diabetes-related traits were estimated among 12 609 participants of the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). A two-sample Mendelian randomization approach incorporated genotype-arsenic metabolism relationships from literature, and genotype-trait relationships from HCHS/SOL, with a mixed-effect linear model. Analyses were stratified by rice consumption and smoking.

RESULTS

Among never smokers with high rice consumption, each percentage point increase in was associated with increases of 1.96 mmHg systolic blood pressure (P = 0.034) and 1.85 mmHg inorganic arsenic diastolic blood pressure (P = 0.003). Monomethylarsenate was associated with increased systolic (1.64 mmHg/percentage point increase; P = 0.021) and diastolic (1.33 mmHg/percentage point increase; P = 0.005) blood pressure. Dimethylarsinate, a marker of efficient metabolism, was associated with lower systolic (-0.92 mmHg/percentage point increase; P = 0.025) and diastolic (-0.79 mmHg/percentage point increase; P = 0.004) blood pressure. Among low rice consumers and ever smokers, the results were consistent with no association. Evidence for a relationship with diabetes was equivocal.

CONCLUSIONS

Less efficient arsenic metabolism was associated with increased blood pressure among never smokers with high rice consumption, suggesting that arsenic exposure through rice may contribute to high blood pressure in the Hispanic/Latino community.

Robustifying Trial-Derived Optimal Treatment Rules for A Target Population

Treatment rules based on individual patient characteristics that are easy to interpret and disseminate are important in clinical practice. Properly planned and conducted randomized clinical trials are used to construct individualized treatment rules. However, it is often a concern that trial participants lack representativeness, so it limits the applicability of the derived rules to a target population. In this work, we use data from a single trial study to propose a two-stage procedure to derive a robust and parsimonious rule to maximize the benefit in the target population. The procedure allows a wide range of possible covariate distributions in the target population, with minimal assumptions on the first two moments of the covariate distribution. The practical utility and favorable performance of the methodology are demonstrated using extensive simulations and a real data application.

Matched Learning for Optimizing Individualized Treatment Strategies Using Electronic Health Records

Current guidelines for treatment decision making largely rely on data from randomized controlled trials (RCTs) studying average treatment effects. They may be inadequate to make individualized treatment decisions in real-world settings. Large-scale electronic health records (EHR) provide opportunities to fulfill the goals of personalized medicine and learn individualized treatment rules (ITRs) depending on patient-specific characteristics from real-world patient data. In this work, we tackle challenges with EHRs and propose a machine learning approach based on matching (M-learning) to estimate optimal ITRs from EHRs. This new learning method performs matching instead of inverse probability weighting as commonly used in many existing methods for estimating ITRs to more accurately assess individuals' treatment responses to alternative treatments and alleviate confounding. Matching-based value functions are proposed to compare matched pairs under a unified framework, where various types of outcomes for measuring treatment response (including continuous, ordinal, and discrete outcomes) can easily be accommodated. We establish the Fisher consistency and convergence rate of M-learning. Through extensive simulation studies, we show that M-learning outperforms existing methods when propensity scores are misspecified or when unmeasured confounders are present in certain scenarios. Lastly, we apply M-learning to estimate optimal personalized second-line treatments for type 2 diabetes patients to achieve better glycemic control or reduce major complications using EHRs from New York Presbyterian Hospital.

Efficient methods for signal detection from correlated adverse events in clinical trials

It is an important and yet challenging task to identify true signals from many adverse events that may be reported during the course of a clinical trial. One unique feature of drug safety data from clinical trials, unlike data from post-marketing spontaneous reporting, is that many types of adverse events are reported by only very few patients leading to rare events. Due to the limited study size, the p-values of testing whether the rate is higher in the treatment group across all types of adverse events are in general not uniformly distributed under the null hypothesis that there is no difference between the treatment group and the placebo group. A consequence is that typically fewer than 100 α percent of the hypotheses are rejected under the null at the nominal significance level of α . The other challenge is multiplicity control. Adverse events from the same body system may be correlated. There may also be correlations between adverse events from different body systems. To tackle these challenging issues, we develop Monte-Carlo-based methods for the signal identification from patient-reported adverse events in clinical trials. The proposed methodologies account for the rare events and arbitrary correlation structures among adverse events within and/or between body systems. Extensive simulation studies demonstrate that the proposed method can accurately control the family-wise error rate and is more powerful than existing methods under many practical situations. Application to two real examples is provided.

Extra-familial social factors and obesity in the Hispanic Community Children's Health Study/Study of Latino Youth

Hispanic/Latino youth are disproportionately affected by obesity. However, how social factors outside of the family relate to Hispanic/Latino youth obesity is not well understood. We examined associations of extra-familial social factors with overweight/obesity prevalence, and their variation by sex and age, in 1444 Study of Latino Youth participants [48.6% female; 43.4% children (8-11 years); 56.6% adolescents (12-16 years)], who were offspring of the Hispanic Community Health Study/Study of Latinos participants. Youth self-reported general social support from friends, dietary-, and physical activity (PA)-specific support from peers, and awareness/internalization of thinness ideals. Overweight/obesity was defined as body mass index ≥ 85th percentile. Logistic regression models assessed effects of social factors and their interactions with age-group and sex, adjusting for potential confounders. Social support from friends interacted with both age and sex in relation to overweight/obesity. Female children who reported lesser (OR 0.60; 95% CI [0.39, 0.91]) and female adolescents who reported greater (OR 1.35; 95% CI [1.06, 1.74]) social support from friends had higher odds of overweight/obesity. Among males, greater awareness/internalization of thinness ideals related to higher odds of overweight/obesity (OR 2.30; 95% CI [1.59, 3.31]). Awareness/internalization of thinness ideals was not associated with overweight/obesity among females. Dietary and PA-specific peer support did not relate to overweight/obesity. Social support from friends and awareness/internalization of thinness ideals were significantly related to odds of overweight/obesity in Hispanic/Latino youth; associations varied by age and sex, and persisted after control for intra-familial factors (overall family support/function; diet and activity specific support).

I-Boost: an integrative boosting approach for predicting survival time with multiple genomics platforms

We propose a statistical boosting method, termed I-Boost, to integrate multiple types of high-dimensional genomics data with clinical data for predicting survival time. I-Boost provides substantially higher prediction accuracy than existing methods. By applying I-Boost to The Cancer Genome Atlas, we show that the integration of multiple genomics platforms with clinical variables improves the prediction of survival time over the use of clinical variables alone; gene expression values are typically more prognostic of survival time than other genomics data types; and gene modules/signatures are at least as prognostic as the collection of individual gene expression data.

Robust Score Tests With Missing Data in Genomics Studies

Analysis of genomic data is often complicated by the presence of missing values, which may arise due to cost or other reasons. The prevailing approach of single imputation is generally invalid if the imputation model is misspecified. In this paper, we propose a robust score statistic based on imputed data for testing the association between a phenotype and a genomic variable with (partially) missing values. We fit a semiparametric regression model for the genomic variable against an arbitrary function of the linear predictor in the phenotype model and impute each missing value by its estimated posterior expectation. We show that the score statistic with such imputed values is asymptotically unbiased under general missing-data mechanisms, even when the imputation model is misspecified. We develop a spline-based method to estimate the semiparametric imputation model and derive the asymptotic distribution of the corresponding score statistic with a consistent variance estimator using sieve approximation theory and empirical process theory. The proposed test is computationally feasible regardless of the number of independent variables in the imputation model. We demonstrate the advantages of the proposed method over existing methods through extensive simulation studies and provide an application to a major cancer genomics study.

Outcome-Weighted Learning for Personalized Medicine with Multiple Treatment Options

To achieve personalized medicine, an individualized treatment strategy assigning treatment based on an individual's characteristics that leads to the largest benefit can be considered. Recently, a machine learning approach, O-learning, has been proposed to estimate an optimal individualized treatment rule (ITR), but it is developed to make binary decisions and thus limited to compare two treatments. When many treatment options are available, existing methods need to be adapted by transforming a multiple treatment selection problem into multiple binary treatment selections, for example, via one-vs-one or one-vs-all comparisons. However, combining multiple binary treatment selection rules into a single decision rule requires careful consideration, because it is known in the multicategory learning literature that some approaches may lead to ambiguous decision rules. In this work, we propose a novel and efficient method to generalize outcome-weighted learning for binary treatment to multi-treatment settings. We solve a multiple treatment selection problem via sequential weighted support vector machines. We prove that the resulting ITR is Fisher consistent and obtain the convergence rate of the estimated value function to the true optimal value, i.e., the estimated treatment rule leads to the maximal benefit when the data size goes to infinity. We conduct simulations to demonstrate that the proposed method has superior performance in terms of lower mis-allocation rates and improved expected values. An application to a three-arm randomized trial of major depressive disorder shows that an ITR tailored to individual patient's expectancy of treatment efficacy, their baseline depression severity and other characteristics reduces depressive symptoms more than non-personalized treatment strategies (e.g., treating all patients with combined pharmacotherapy and psychotherapy).

Counting process-based dimension reduction methods for censored outcomes

We propose counting process-based dimension reduction methods for right-censored survival data. Semiparametric estimating equations are constructed to estimate the dimension reduction subspace for the failure time model. Our methods address two limitations of existing approaches. First, using the counting process formulation, they do not require estimation of the censoring distribution to compensate for the bias in estimating the dimension reduction subspace. Second, the nonparametric estimation involved adapts to the structural dimension, so our methods circumvent the curse of dimensionality. Asymptotic normality is established for the estimators. We propose a computationally efficient approach that requires only a singular value decomposition to estimate the dimension reduction subspace. Numerical studies suggest that our new approaches exhibit significantly improved performance. The methods are implemented in the [Formula: see text] package [Formula: see text].

Associations of Mitochondrial and Nuclear Mitochondrial Variants and Genes with Seven Metabolic Traits

Mitochondria (MT), the major site of cellular energy production, are under dual genetic control by 37 mitochondrial DNA (mtDNA) genes and numerous nuclear genes (MT-nDNA). In the CHARGEmtDNA+ Consortium, we studied genetic associations of mtDNA and MT-nDNA associations with body mass index (BMI), waist-hip-ratio (WHR), glucose, insulin, HOMA-B, HOMA-IR, and HbA1c. This 45-cohort collaboration comprised 70,775 (insulin) to 170,202 (BMI) pan-ancestry individuals. Validation and imputation of mtDNA variants was followed by single-variant and gene-based association testing. We report two significant common variants, one in MT-ATP6 associated (p ≤ 5E-04) with WHR and one in the D-loop with glucose. Five rare variants in MT-ATP6, MT-ND5, and MT-ND6 associated with BMI, WHR, or insulin. Gene-based meta-analysis identified MT-ND3 associated with BMI (p ≤ 1E-03). We considered 2,282 MT-nDNA candidate gene associations compiled from online summary results for our traits (20 unique studies with 31 dataset consortia's genome-wide associations [GWASs]). Of these, 109 genes associated (p ≤ 1E-06) with at least 1 of our 7 traits. We assessed regulatory features of variants in the 109 genes, cis- and trans-gene expression regulation, and performed enrichment and protein-protein interactions analyses. Of the identified mtDNA and MT-nDNA genes, 79 associated with adipose measures, 49 with glucose/insulin, 13 with risk for type 2 diabetes, and 18 with cardiovascular disease, indicating for pleiotropic effects with health implications. Additionally, 21 genes related to cholesterol, suggesting additional important roles for the genes identified. Our results suggest that mtDNA and MT-nDNA genes and variants reported make important contributions to glucose and insulin metabolism, adipocyte regulation, diabetes, and cardiovascular disease.

SEMIPARAMETRIC TRANSFORMATION MODELS WITH MULTILEVEL RANDOM EFFECTS FOR CORRELATED DISEASE ONSET IN FAMILIES

Large cohort studies are commonly launched to study risk of genetic variants or other risk factors on age at onset (AAO) of a chronic disorder. In these studies, family history data including AAO of disease in family members are collected to provide additional information and can be used to improve efficiency. Statistical analysis of these data is challenging due to missing genotypes in family members and the heterogeneous dependence attributed to both shared genetic back-ground and shared environmental factors (e.g., life style). In this paper, we propose a class of semiparametric transformation models with multilevel random effects to tackle these challenges. The proposed models include both proportional hazards model and proportional odds model as special cases. The multilevel random effects contain individual-specific random effects including kinship correlation structure dependent on the family pedigree, and a shared random effect to account for unobserved environment exposure. We use nonparametric maximum likelihood approach for inference and propose an expectation-maximization algorithm for computation in the presence of missing genotypes among family members. The obtained estimators are shown to be consistent, asymptotically normal, and semiparametrically efficient. Simulation studies demonstrate that the proposed method performs well with finite sample sizes. Finally, the proposed method is applied to study genetic risks in an Alzheimer's disease study.

Prevalence of pharmacogenomic variants affecting the efficacy of clopidogrel therapy in the Hispanic Community Health Study/Study of Latinos cohort

PURPOSE

Although clopidogrel is the most widely used oral P2Y12 receptor antagonist, up to 10% of acute coronary syndrome patients treated with clopidogrel will experience a recurrent myocardial infarction and 2-3% will experience stent thrombosis within 1 year. The purpose of this research is to describe the prevalence of pharmacogene variants associated with clopidogrel responsiveness (CYP2C19, B4GALT2, ABCB1, PON1, CES1 and P2RY12) in Hispanic/Latino patients of diverse backgrounds.

METHODS

Minor allele frequencies of nine variants from participants of Hispanic Community Health Study/Study of Latinos were compared between subpopulations as well as to continental ancestry references using z-test for independent proportions.

RESULTS

MAFs for six out of nine variants differed between Caribbean and Mainland subpopulations (p < 0.05). Compared with European reference group, MAFs of ABCB1, CES1 and PON1 were higher in Hispanic Community Health Study/Study of Latinos, whereas B4GALT2 and CYP2C19*2 and *17 were lower.

CONCLUSION

Significant differences in the prevalence of most pharmacogenomic variants related to clopidogrel response provide a foundation to better inform ongoing and future clinical studies of clopidogrel pharmacogenetics in the US Hispanic/Latino populations.

Proper Inference for Value Function in High-Dimensional Q-Learning for Dynamic Treatment Regimes

Dynamic treatment regimes are a set of decision rules and each treatment decision is tailored over time according to patients' responses to previous treatments as well as covariate history. There is a growing interest in development of correct statistical inference for optimal dynamic treatment regimes to handle the challenges of non-regularity problems in the presence of non-respondents who have zero-treatment effects, especially when the dimension of the tailoring variables is high. In this paper, we propose a high-dimensional Q-learning (HQ-learning) to facilitate the inference of optimal values and parameters. The proposed method allows us to simultaneously estimate the optimal dynamic treatment regimes and select the important variables that truly contribute to the individual reward. At the same time, hard thresholding is introduced in the method to eliminate the effects of the non-respondents. The asymptotic properties for the parameter estimators as well as the estimated optimal value function are then established by adjusting the bias due to thresholding. Both simulation studies and real data analysis demonstrate satisfactory performance for obtaining the proper inference for the value function for the optimal dynamic treatment regimes.

Estimating Treatment Effects for Recurrent Events in the Presence of Rescue Medications: An Application to the Immune Thrombocytopenia Study

In many clinical studies, patients may experience the same type of event of interest repeatedly over time. However, the assessment of treatment effects is often complicated by the rescue medication uses due to ethical reasons. For example, in the motivating trial in studying the Immune Thrombocytopenia (ITP), when the interest lies in evaluating the treatment benefit of investigational product (IP) on reducing patient's repeated bleeding, rescue medication such as platelet transfusions may be allowed to raise platelet counts. Both the intention-to-treat analysis and treating the intermediate rescue medication as covariate tend to attenuate the treatment benefit, and the estimates can be biased if interpreted as causal. In this paper, we propose a general causal framework when intermediate rescue medications are informative. We adopt the inverse weighted estimation approach to estimate the treatment effect, where weights are constructed to reflect time-dependent medication use probabilities. The proposed estimators are shown to be asymptotically normal and are demonstrated to perform well in small-sample simulation studies. The application to the ITP studies reveals a stronger benefit of using IP in reducing bleeding.

Learning Subject-Specific Directed Acyclic Graphs With Mixed Effects Structural Equation Models From Observational Data

The identification of causal relationships between random variables from large-scale observational data using directed acyclic graphs (DAG) is highly challenging. We propose a new mixed-effects structural equation model (mSEM) framework to estimate subject-specific DAGs, where we represent joint distribution of random variables in the DAG as a set of structural causal equations with mixed effects. The directed edges between nodes depend on observed exogenous covariates on each of the individual and unobserved latent variables. The strength of the connection is decomposed into a fixed-effect term representing the average causal effect given the covariates and a random effect term representing the latent causal effect due to unobserved pathways. The advantage of such decomposition is to capture essential asymmetric structural information and heterogeneity between DAGs in order to allow for the identification of causal structure with observational data. In addition, by pooling information across subject-specific DAGs, we can identify causal structure with a high probability and estimate subject-specific networks with a high precision. We propose a penalized likelihood-based approach to handle multi-dimensionality of the DAG model. We propose a fast, iterative computational algorithm, DAG-MM, to estimate parameters in mSEM and achieve desirable sparsity by hard-thresholding the edges. We theoretically prove the identifiability of mSEM. Using simulations and an application to protein signaling data, we show substantially improved performances when compared to existing methods and consistent results with a network estimated from interventional data. Lastly, we identify gray matter atrophy networks in regions of brain from patients with Huntington's disease and corroborate our findings using white matter connectivity data collected from an independent study.

Structural social support and cardiovascular disease risk factors in Hispanic/Latino adults with diabetes: results from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL)

OBJECTIVE(S)

Cross-sectional and longitudinal studies have yielded inconsistent findings on the associations of social support networks with cardiovascular health in Hispanic/Latino adults with diabetes. We examined the cross-sectional associations of structural social support and traditional cardiovascular disease (CVD) risk factors in a diverse sample of Hispanic/Latino adults with diabetes.

RESEARCH DESIGN AND METHODS

This analysis included 2994 adult participants ages 18-74 with diabetes from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL - 2008-2011). Select items from the Social Network Inventory (SNI) were used to assess indices of structural social support, i.e. network size (number of children, parents, and in-laws) and frequency of familial contact. Standardized methods were used to measure abdominal obesity, BMI, hypertension, hypercholesterolemia, and smoking status. Multivariate regression was used to examine associations of structural support with individual CVD risk factors with demographics, acculturation, physical health, and psychological ill-being (depressive symptoms and anxiety) included as covariates.

RESULTS

There were no significant cross-sectional associations of structural support indices with abdominal obesity, hypertension, hypercholesterolemia, or smoking status. There was a marginally significant (OR: 1.05; 95%CI 0.99-1.11) trend toward higher odds of obesity in participants reporting a larger family unit (including children, parents, and in-laws) and those with closer ties with extended family relatives (OR: 1.04; 95%CI 0.99-1.09).

CONCLUSIONS

Structural social support was marginally associated with higher odds of obesity in Hispanic/Latino adults with diabetes. Alternate forms of social support (e.g. healthcare professionals, friends, peers) should be further explored as potential markers of cardiac risk in Hispanics/Latinos with diabetes.

Sleep disordered breathing and fibroblast growth factor 23 in the Hispanic Community Health Study/Study of Latinos

Preclinical data suggest that hypoxia stimulates fibroblast growth factor 23 (FGF23) transcription and cleavage in osteocytes, resulting in elevated circulating c-terminal (cFGF23) levels but normal intact FGF23 (iFGF23) levels. We conducted a case-control study within the Hispanic Community Health Study/Study of Latinos to investigate whether sleep disordered breathing, as a model of hypoxemia, is independently associated with elevated cFGF23 levels in the general population and with elevated cFGF23 and iFGF23 levels in patients with chronic kidney disease (CKD), in whom FGF23 cleavage may be impaired. Cases (n = 602) had severe sleep disordered breathing defined as an apnea/hypopnea index (AHI) of ≥30. Controls without severe sleep disordered breathing (n = 602) were matched for sex and CKD stage. The median AHI in the cases was 45.8 (IQR 35.5-62.5) compared to 2.6 (IQR 0.6-8.2) in the controls. Cases had higher cFGF23 levels than controls (66.2 RU/mL, IQR 52.8-98.4 vs. 61.2 RU/mL, IQR 49.5-80.1, p value <0.001). There were no differences in iFGF23 levels between cases and controls. In adjusted linear regression and multinomial regression analyses, body mass index attenuated the relationship between severe sleep disordered breathing and cFGF23 levels. No significant relationships were seen in analyses of severe sleep disordered breathing and iFGF23 levels or in analyses of iFGF23 and cFGF23 stratified by CKD status. Additional studies using other models of intermittent and chronic hypoxia are needed to confirm whether hypoxia stimulates FGF23 transcription in humans and to determine the impact on iFGF23 levels in CKD.

Semiparametric Regression Analysis of Multiple Right- and Interval-Censored Events

Health sciences research often involves both right- and interval-censored events because the occurrence of a symptomatic disease can only be observed up to the end of follow-up, while the occurrence of an asymptomatic disease can only be detected through periodic examinations. We formulate the effects of potentially time-dependent covariates on the joint distribution of multiple right- and interval-censored events through semiparametric proportional hazards models with random effects that capture the dependence both within and between the two types of events. We consider nonparametric maximum likelihood estimation and develop a simple and stable EM algorithm for computation. We show that the resulting estimators are consistent and the parametric components are asymptotically normal and efficient with a covariance matrix that can be consistently estimated by profile likelihood or nonparametric bootstrap. In addition, we leverage the joint modelling to provide dynamic prediction of disease incidence based on the evolving event history. Furthermore, we assess the performance of the proposed methods through extensive simulation studies. Finally, we provide an application to a major epidemiological cohort study. Supplementary materials for this article are available online.

Efficient Estimation for Semiparametric Structural Equation Models With Censored Data

Structural equation modeling is commonly used to capture complex structures of relationships among multiple variables, both latent and observed. We propose a general class of structural equation models with a semiparametric component for potentially censored survival times. We consider nonparametric maximum likelihood estimation and devise a combined Expectation-Maximization and Newton-Raphson algorithm for its implementation. We establish conditions for model identifiability and prove the consistency, asymptotic normality, and semiparametric efficiency of the estimators. Finally, we demonstrate the satisfactory performance of the proposed methods through simulation studies and provide an application to a motivating cancer study that contains a variety of genomic variables. Supplementary materials for this article are available online.