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Activity Number: 33 - Cutting-Edge Statistical Methods for Modeling Disease Progression Processes
Type: Contributed
Date/Time: Sunday, July 30, 2017 : 2:00 PM to 3:50 PM
Sponsor: International Chinese Statistical Association
Abstract #322723
Title: A Mechanistic Nonlinear Model for Censored and Mis-Measured Covariates in Longitudinal Models, with Application in AIDS Studies
Author(s): Hongbin Zhang* and Hubert Wong and Lang Wu
Companies: CUNY (SPH) and CIHR CTN and UBC
Keywords: AIDS ; Censoring ; Measurement error ; Nonlinear mixed effects models ; Joint model

When modeling longitudinal data, the true values of time-varying covariates may be unknown due to detection-limit censoring or measurement error. A common approach in the literature is to empirically model the covariate process based on observed data, and then predict the censored values or mis-measured values. Such an empirical model can be misleading, especially for censored values since the (unobserved) censored values may behave very differently than observed values due to the underlying data-generation mechanisms or disease status. In this paper, we propose a mechanistic nonlinear covariate model based on the underlying data-generation mechanisms to address censored values and mis-measured values. Such a mechanistic model is based on solid scientific or biological arguments, so the predicted censored or mis-measured values are more reasonable. We use a Monte Carlo EM algorithm for likelihood inference, and apply the methods to an AIDS dataset, where viral load is censored by a detection limit. Simulation results confirm that the proposed models, methods offer substantial advantages over existing empirical covariate models for censored and mis-measured covariates.

Authors who are presenting talks have a * after their name.

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