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DTSTAMP:20260715T114536Z
DESCRIPTION:\n	Abstract:\n\n\nDespite the success of combined antiretroviral
  therapy (ART) in achieving sustained control of viral replication\, the c
 oncerns 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 sta
 bilize at a viral load set point. Characterizing features of the viral reb
 ound trajectories (e.g.\, time to viral rebound and viral set points) and 
 identifying host\, virological\, and immunological factors that are predic
 tive of these features requires addressing analytical challenges such as n
 on-linear viral rebound trajectories\, coarsened data due to the assay’s l
 imit of quantification\, and intermittent measurements of viral load value
 s. We first introduce a parametric nonlinear mixed effects (NLME) model fo
 r the viral rebound trajectory and compare its performance to a mechanisti
 c modeling approach. We then develop a smoothed simulated pseudo maximum l
 ikelihood method for fitting NLME models that permits flexible specificati
 on of random effects distributions. Finally\, we investigate the associati
 on 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-ce
 nsored observations.\n\n\n	Speaker\n\n\nDr. Rui Wang is an Associate Profes
 sor of Population Medicine and Director of the Division of Biostatistics i
 n 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 H
 ealth. Dr. Wang received her PhD Degree from Harvard University.\n\nDr. Wa
 ng’s research interests include the design\, monitoring\, and analysis of 
 parallel and stepped-wedge cluster randomized trials\, where a group of su
 bjects\, as opposed to individuals\, are randomized to each of the treatme
 nt arms in the trial. The particular questions she is addressing include t
 he investigation of how the complex correlation structure within clusters 
 affects the sample size and power of the trial\, and how to analyze data f
 rom such trials efficiently\, taking into account the correlation structur
 e and the issue of missing data. She has also been developing improved sta
 tistical techniques for a cross-sectional approach that\, when combined wi
 th modern HIV screening methods\, can substantially reduce the cost and in
 crease the accuracy of HIV incidence estimation. Her research interests al
 so include longitudinal modeling of non-linear trajectories and model sele
 ction\, as well as addressing missing data issues in distributed data netw
 orks.\n\nZoom Link\n\nMeeting ID: 924 5390 4989\n\nPasscode: 690084\n\n \n
 \n \n
DTSTART:20201120T203000Z
DTEND:20201120T213000Z
SUMMARY:Rui Wang 
URL:https://www.mcgill.ca/mathstat/channels/event/rui-wang-326332
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