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Activity Number: 430
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #320234 View Presentation
Title: Model Estimation and Dynamic Prediction for Subject-Specific Event Probability in Joint Modeling Using Longitudinal Quantile Regression
Author(s): Ming Yang* and Sheng Luo and Stacia DeSantis
Companies: The University of Texas Health Science Center at Houston and The University of Texas at Houston and The University of Texas Health Science Center at Houston
Keywords: Asymmetrical Laplace distribution ; Dynamic prediction ; Huntington's disease ; Joint model ; Linear quantile mixed model

In the conventional joint model (JM) of a longitudinal and time-to-event outcome, a linear mixed model (LMM) assuming normal random error is typically used to model the longitudinal process. However, in many circumstances, the normality assumption cannot be satisfied and the LMM is not the appropriate submodel. In addition, as LMM models the conditional mean of the longitudinal outcome, it is not appropriate if clinical interest lies in making inference or prediction about medians, lower, or upper ends of the response distribution. Quantile regression (QR), on the other hand, provides a flexible, distribution-free way to study covariate effects at different quantiles of the longitudinal outcome that is robust to deviations from assumed normality or errors, and to outlying observations. In this paper, we present and advocate the linear quantile mixed model (LQMM) for the longitudinal process in the JM framework. Our development is motivated by large prospective study of Huntington's Disease where primary clinical interest is in utilizing longitudinal motor scores and other early covariates to predict the risk of developing HD. To this end, we develop a Gibbs sampler based on the loc

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

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