Analytic Methods for HIV Treatment and Co-Factor Effects

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DESCRIPTION (provided by applicant): The principal aim of this proposal is to further development of new methods for analyzing observational data bases and randomized trials of HIV-infected persons and the application of these methods to data obtained in randomized and observational studies in an attempt to help answer important open substantive questions concerning the treatment and course of HIV-related disease. The proposed approaches are based either on (i) the estimation of new classes of causal models which include structural nested models, marginal structural models (MSMs), direct effect structural nested models, continuous time structural nested models, and optimal regime structural models (SNMs). Many of the new methods are fundamentally "epidemiologic" in that they require data on time-dependent confounding factors, that is, risk factors for outcomes that also predict subsequent treatment with the drug or cofactor under study. In particular, we plan to use optimal regime SNMs to help determine the optimal times to start HAART therapy and to change HAART regimens as a function of a subject's CD4 count, HIV RNA, clinical history, and, where available, results of genotypic or phenotypic resistance testing. Specifically, we plan to reanalyze, with collaborators, data from the Multicenter AIDS Cohort Study, The Women's Interagency HIV Study, The Swiss HIV Cohort Study, The Study of The Consequences of Protease Inhibitor Era (SCOPE), the French Hospital Database on HIV (FHDH) study, the Pediatric Late Outcomes Protocol (PACTG 219) and the ALLRT study.