The world urgently needs to advance the HIV cure research agenda to address the persistently high global HIV prevalence and associated mortality. 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) after analytic antiretroviral treatment interruption (ATI) and identifying host, virological, and immunological factors that are predictive of these features are central to HIV cure research. But doing so requires addressing a variety of analytical challenges, including the non-linear viral rebound trajectories, coarsened data due to the assay's limit of quantification, intermittent measurements of viral load values, small sample sizes from individual studies, and high-dimensional candidate predictors. Motivated by our ongoing collaborations with HIV cure research investigators and built on our previous work, we aim to address key methodological gaps by leveraging data from multiple randomized studies conducted by the AIDS Clinical Trials Group and from the Zurich Primary HIV Infection Cohort. Aim 1 proposes to develop a new set of methods for prediction of time to viral rebound based on comprehensive history profiles, such as the rate of viral decay after ART initiation, extending fitting algorithms and variable selection techniques developed for interval-censored outcomes. Aim 2 proposes to fit the viral rebound model using a Smoothed Simulated Pseudo Maximum likelihood method which maximizes a smoothed simulated objective function constructed based on a Monte Carlo approximation of the first two moments of the smoothed responses, and to develop methods to assess the association between time to rebound and the viral set point and to simultaneously select biomarkers that affect different finer features of the viral rebound trajectory. Aim 3 proposes to develop methods that optimally integrate data from multiple cohorts and different phases of viral load trajectories while properly accounting for the homogeneity and heterogeneity in covariate effects across studies. Innovation lies in the development and application of new methods for modeling viral rebound that address various inherent challenges in analyses of available data. Significance lies in the role of these methods in better characterizing viral rebound trajectories, identifying pre- ATI predictors, and assessing the effects and mechanisms of novel therapeutic agents. The results of the proposed research can inform optimal design of future ATI studies and provide new tools that can extract more inform...