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Despite the success of combined antiretroviral therapy (ART) in achieving sustained control of viral replication, the concerns about side-effects, drug-drug interactions, drug resistance and cost call for a need to identify strategies for achieving HIV eradication or an ART-free remission. Following ART withdrawal, patients’ viral load levels usually increase rapidly to a peak followed by a dip, and then stabilize at a viral load set point. Characterizing features of the viral rebound trajectories (e.g., time to viral rebound and viral set points) and identifying host, virological, and immunological factors that are predictive of these features requires addressing analytical challenges such as non-linear viral rebound trajectories, coarsened data due to the assay’s limit of quantification, and intermittent measurements of viral load values. We first introduce a parametric nonlinear mixed effects (NLME) model for the viral rebound trajectory and compare its performance to a mechanistic modeling approach. We then develop a smoothed simulated pseudo maximum likelihood method for fitting NLME models that permits flexible specification of random effects distributions. Finally, we investigate the association between the time to viral suppression after ART initiation and the time to viral rebound after ART interruption through a Cox proportional hazard regression model where both the outcome and the covariate are interval-censored observations.

Dr. Rui Wang is an Associate Professor of Population Medicine and Director of the Division of Biostatistics in the Department of Population Medicine at Harvard Medical School and the Harvard Pilgrim Health Care Institute. She is also an Associate Professor in the Department of Biostatistics at Harvard T.H. Chan School of Public Health. Dr. Wang received her PhD Degree from Harvard University.

Dr. Wang’s research interests include the design, monitoring, and analysis of parallel and stepped-wedge cluster randomized trials, where a group of subjects, as opposed to individuals, are randomized to each of the treatment arms in the trial. The particular questions she is addressing include the investigation of how the complex correlation structure within clusters affects the sample size and power of the trial, and how to analyze data from such trials efficiently, taking into account the correlation structure and the issue of missing data. She has also been developing improved statistical techniques for a cross-sectional approach that, when combined with modern HIV screening methods, can substantially reduce the cost and increase the accuracy of HIV incidence estimation. Her research interests also include longitudinal modeling of non-linear trajectories and model selection, as well as addressing missing data issues in distributed data networks.

Meeting ID: 924 5390 4989

Passcode: 690084

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