The objective of this project is to develop and apply statistical methods in AIDS research to improve scientific inferences when longitudinal follow-up designs are employed. The proposed research includes the following aims: (1) To develop statistical methods for marker data with informative terminal events. In longitudinal studies of HIV/AIDS research, marker measurements are frequently collected or observed conditioning on the occurrence of recurrent events. Two types of marker measurements will be considered under Aim 1: recurrent- marker process data, and post-treatment marker data evaluated at time of failure event. The collection of marker data is typically terminated by administrative censoring or occurrence of a terminal event such as death, where the terminal event is possibly correlated with the marker measurements of interest. The work under Aim 1 will include the development of inference, modeling and estimation methods for analyzing recurrent marker process data, and analytical procedures for estimation of causal treatment effects from post-treatment marker data. (2) To develop new group sequential methods for monitoring censored time-to-event endpoints in long-term HIV/AIDS clinical trials. Interim analyses are usually required by the Data and Safety Monitoring Board (DSMB) to monitor the efficacy and safety of a prevention regiment or therapeutic treatment in long-term HIV/AIDS clinical trials. For such long-term HIV/AIDS trials that collect censored time-to-event endpoints, naive applications of the conventional statistical methods, such as the proportional hazards model (Cox, 1972), are often insufficient to characterize the time-varying nature of treatment effect between different treatment regiments during long-term follow-up. Under this aim, we will develop new group sequential methods based on the hazard functions with change points in monitoring time-varying treatment effect for censored time-to-event outcomes. (3) To develop regression methods to accommodate evolving covariate effects for recurrent events data. HIV/AIDS interventions rarely have constant effects. Whether it is an HIV prevention trial or AIDS therapeutic study, it is unrealistic to expect the intervention to take full effect instantaneously after randomization. Furthermore, drug resistance might develop over time, which erodes the intervention effect. Characterizing and quantifying time- varying intervention effect would provide valuable scientific insight to the mechanism of the intervention. However, most available methods only accommodate constant effects. We plan to develop statistical models and inference procedures to address this issue, with a focus on generalizing the accelerated failure time model and on recurrent events data. (4) To develop enhanced sensitivity analysis procedures for analyzing HIV randomized studies with premature loss of follow-up, especially due to termination of treatment. In many registration trials for FDA approval of HIV medicines, patients are not followed after treatment termination. Until the FDA mandates continued follow-up of patients after treatment termination, the aim of this research is to provide FDA clinical reviewers and the broader scientific community information about intention to treat effects that would otherwise be unavailable. The methods we will develop will be generally applicable to randomized trials and observational studies with potentially informative loss of follow-up.
New statistical models and methods are proposed to study survival, recurrent events and marker process data in HIV/AIDS clinical trials and cohort studies. Statistical tools and techniques are developed to deal with some of the sophisticated and important problems arising in AIDS studies with longitudinal nature.
|Cheng, Yu-Jen; Wang, Mei-Cheng (2015) Causal estimation using semiparametric transformation models under prevalent sampling. Biometrics 71:302-12|
|Huang, Yijian (2014) CORRECTED SCORE WITH SIZABLE COVARIATE MEASUREMENT ERROR: PATHOLOGY AND REMEDY. Stat Sin 24:357-374|
|Huang, Yijian (2013) Fast Censored Linear Regression. Scand Stat Theory Appl 40:|
|Chan, Kwun Chuen Gary; Wang, Mei-Cheng (2012) Estimating incident population distribution from prevalent data. Biometrics 68:521-31|
|Cheng, Yu-Jen; Wang, Mei-Cheng (2012) Estimating propensity scores and causal survival functions using prevalent survival data. Biometrics 68:707-16|
|Wang, Mei-Cheng; Li, Shanshan (2012) Bivariate marker measurements and ROC analysis. Biometrics 68:1207-18|
|Gary Chan, Kwun Chuen; Wang, Mei-Cheng (2010) BACKWARD ESTIMATION OF STOCHASTIC PROCESSES WITH FAILURE EVENTS AS TIME ORIGINS. Ann Appl Stat 4:1602-1620|
|Chen, Ying Qing (2010) Semiparametric regression in size-biased sampling. Biometrics 66:149-58|
|Huang, Yijian (2010) QUANTILE CALCULUS AND CENSORED REGRESSION. Ann Stat 38:1607-1637|
|Huang, C-Y; Qin, J; Wang, M-C (2010) Semiparametric analysis for recurrent event data with time-dependent covariates and informative censoring. Biometrics 66:39-49|
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