Statistical Methods in HIV/AIDS Research Project Summary The objective of this methods proposal is to develop and apply new statistical methods to improve scientific inferences for data collected in HIV/AIDS research, specifically aiming to: (1) develop a general class of regression models to study residual time in HIV treatment and prevention efficacy trials. The developed methods will be applied to determine in residual time whether early versus delayed ART initiation is important in patient's time to AIDS-defining illness or death, and to further explore the clinical factors which are important in governing the progression to AIDS or death after ART initiation;(2) develop statistical methods for recurrent event and marker process data that exhibit a terminal behavior before an informative failure event. The developed methods will be applied to examine the correlation between treatment regimens and the complete history of recurrent events such as opportunistic infections, taking into account variables available to clinicians in the course of routine care;(3) develop statistical methodologies to infer causal treatment effect from complex longitudinal data in randomized HIV prevention trials.

Public Health Relevance

Statistical Methods in HIV/AIDS Research Project Narrative This proposal aims to develop statistical methods in HIV/AIDS research. Specifically, these methods are targeted to regression analysis of censored residual life, analysis of recurrent markers in the presence of terminal events, and informative censoring and causal inferences of treatment efficacy in complex HIV/AIDS clinical trials. Upon successful completion of the project, the proposed methods shall provide a collection of new statistical tools for resource planning, clinical consulting and efficacy assessment in HIV/AIDS research.

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
Research Project (R01)
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AIDS Clinical Studies and Epidemiology Study Section (ACE)
Program Officer
Gezmu, Misrak
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Fred Hutchinson Cancer Research Center
United States
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Crouch, Luis Alexander; Zheng, Cheng; Chen, Ying Qing (2017) Estimating a Treatment Effect in Residual Time Quantiles under the Additive Hazards Model. Stat Biosci 9:298-315
Chan, Kwun Chuen Gary; Wang, Mei-Cheng (2017) Semiparametric modeling and estimation of the terminal behavior of recurrent marker processes before failure events. J Am Stat Assoc 112:351-362
Crouch, Luis Alexander; May, Susanne; Chen, Ying Qing (2016) On estimation of covariate-specific residual time quantiles under the proportional hazards model. Lifetime Data Anal 22:299-319
Safren, Steven A; Mayer, Kenneth H; Ou, San-San et al. (2015) Adherence to Early Antiretroviral Therapy: Results From HPTN 052, a Phase III, Multinational Randomized Trial of ART to Prevent HIV-1 Sexual Transmission in Serodiscordant Couples. J Acquir Immune Defic Syndr 69:234-40
Grant, Shannon; Chen, Ying Qing; May, Susanne (2014) Performance of goodness-of-fit tests for the Cox proportional hazards model with time-varying covariates. Lifetime Data Anal 20:355-68
Saegusa, Takumi; Di, Chongzhi; Chen, Ying Qing (2014) Hypothesis testing for an extended cox model with time-varying coefficients. Biometrics 70:619-28
Chan, Kwun Chuen Gary (2014) A note about the identifiability of causal effect estimates in randomized trials with non-compliance. Stat Methodol 16:
Dai, James Y; Gilbert, Peter B; Hughes, James P et al. (2013) Estimating the efficacy of preexposure prophylaxis for HIV prevention among participants with a threshold level of drug concentration. Am J Epidemiol 177:256-63
Chan, Kwun Chuen Gary; Wang, Mei-Cheng (2012) Estimating incident population distribution from prevalent data. Biometrics 68:521-31
Dai, James Y; Hughes, James P (2012) A unified procedure for meta-analytic evaluation of surrogate end points in randomized clinical trials. Biostatistics 13:609-24

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