Our research aims to develop and evaluate statistical methods for analyzing high-dimensional data in HIV research. The vast array of viral and host specific biological information now available presents an exciting opportunity to tailor treatment decisions to the specific characteristics of patients and their infecting viral populations. However, how best to use this information creates an analytic challenge due to the large number of potentially relevant parameters and the complex, uncharacterized relationships among them. Our research will integrate and advance several analytic methods including cluster analysis, recursive partitioning, mixed effects modeling, Markov modeling and latent class modeling to arrive ultimately at the best strategies for delaying clinical disease and death. Through the development of novel statistical methods, we will draw from information on viral genetic sequences and cellular immune modulation to achieve the following specific aims: (1) To characterize the progression from sensitive to resistant virus over time and the mediating role of treatment exposure through (1a) combining dimension reduction techniques and Markov models and (1b) extending the latent transition modeling framework to handle an individual belonging to multiple states at a single time point and (2) To assess the predictive contribution of cellular immune modulation on changes in CD4 count over time through (2a) extending prediction based classification to the correlated data setting and (2b) extending the latent class model to accommodate changes in state over time. Our methods will apply broadly to several areas of HIV/AIDS research. The proposed research will include the application of our methods to two clinical data settings: (1) a publicly available viral genetics dataset obtained during 3 clinical studies of Efavirenz and (2) a subset of data currently being collected in a clinical study comparing structured treatment interruption to continuous therapy in HIV patients.
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