Thirty years after identification of HIV, the AIDS epidemic continues with an estimated 34 million individuals currently infected worldwide and 2.5 million new infections each year. Research to prevent and treat HIV infection has grown increasingly sophisticated and the analytic challenges have become correspondingly complex. This application is intended to address timely and important statistical issues in HIV/AIDS research. In particular, novel statistical methods will be developed for (i) analysis of data from studies of pre-exposure prophylaxis (PrEP), (ii) optimal design and analysis of studies with complex sampling and observation schemes, and (iii) optimal design of pooling studies. First, statistical methods are proposed that address key challenges in PrEP studies, namely, inferring drug efficacy in the face of incomplete adherence, combining disparate sources of information to estimate adherence and summarizing adherence patterns. The approaches include causal modeling, Bayesian synthesis of information and functional data analysis. The proposed methods have the potential to provide clarity in a field which is hindered by disparate trial results. Second, becaue HIV studies often utilize expensive tests and assays, the proposed research will explore optimal sampling designs and efficient semiparametric and nonparametric methods for studies that use such two-phase or multi-phase sampling. The problems of multi-phase sampling and competing risks in the context of interval censored data will also be studied. These methods will provide critical guidance for optimizing the use of resources and minimizing costs in studies that utilize expensive assays. Finally, the proposed research will investigate statistical methods for the analysis of pooled data with specific applications to epitope mapping and monitoring for virologic failure in resource constrained settings. The former is a critical component of HIV vaccine development and the latter has the potential to greatly improve health care of HIV-infected individuals in developing countries. The proposed research reflects the extensive and ongoing involvement of the investigators in the field of HIV/AIDS research. The statistical problems addressed are motivated by applications in key areas of HIV research. The solutions outlined are highly innovative and directly applicable to current scientific research in vaccine development, trials of pre-exposure prophylaxis, and other HIV/AIDS trials and studies.

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
Research Project (R01)
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Special Emphasis Panel (ZRG1)
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Gezmu, Misrak
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University of Washington
Biostatistics & Other Math Sci
Schools of Public Health
United States
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