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.

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)
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Gezmu, Misrak
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University of Massachusetts Amherst
Public Health & Prev Medicine
Schools of Public Health
United States
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Papasavvas, Emmanouil; Foulkes, Andrea; Yin, Xiangfan et al. (2015) Plasmacytoid dendritic cell and functional HIV Gag p55-specific T cells before treatment interruption can inform set-point plasma HIV viral load after treatment interruption in chronically suppressed HIV-1(+) patients. Immunology 145:380-90
Azzoni, Livio; Foulkes, Andrea S; Firnhaber, Cynthia et al. (2012) Antiretroviral therapy interruptions result in loss of protective humoral immunity to neoantigens in HIV-infected individuals. AIDS 26:1355-62
Liu, Yan; Foulkes, Andrea S (2011) Latent variable modeling paradigms for genotype-trait association studies. Biom J 53:838-54
Firnhaber, Cynthia; Azzoni, Livio; Foulkes, Andrea S et al. (2011) Randomized trial of time-limited interruptions of protease inhibitor-based antiretroviral therapy (ART) vs. continuous therapy for HIV-1 infection. PLoS One 6:e21450
Azzoni, Livio; Foulkes, Andrea S; Firnhaber, Cynthia et al. (2011) Metabolic and anthropometric parameters contribute to ART-mediated CD4+ T cell recovery in HIV-1-infected individuals: an observational study. J Int AIDS Soc 14:37
Azzoni, Livio; Crowther, Nigel J; Firnhaber, Cynthia et al. (2010) Association between HIV replication and serum leptin levels: an observational study of a cohort of HIV-1-infected South African women. J Int AIDS Soc 13:33
Foulkes, A S; Azzoni, L; Li, X et al. (2010) Prediction based classification for longitudinal biomarkers. Ann Appl Stat 4:1476-1497
Nonyane, Bareng A S; Foulkes, Andrea S (2008) Application of two machine learning algorithms to genetic association studies in the presence of covariates. BMC Genet 9:71
Foulkes, A S; Yucel, R; Reilly, M P (2008) Mixed modeling and multiple imputation for unobservable genotype clusters. Stat Med 27:2784-801
Li, Xiaohong; Foulkes, Andrea S; Yucel, Recai M et al. (2007) An expectation maximization approach to estimate malaria haplotype frequencies in multiply infected children. Stat Appl Genet Mol Biol 6:Article33

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