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Activity Number: 196 - SPEED: Biometrics and Biostatistics Part 2
Type: Contributed
Date/Time: Monday, July 29, 2019 : 11:35 AM to 12:20 PM
Sponsor: Section on Statistics in Epidemiology
Abstract #307588
Title: Hierarchical Likelihood Approach for Joint Models of Longitudinal Non-Survival Responses and Survival Data: a Semiparametric Model with Gamma Shared Random Effects
Author(s): Karl Stessy Bisselou* and Hongying Dai and Gleb Haynatzki
Companies: University of Nebraska Medical Center and University of Nebraska Medical Center and University of Nebraska Medical Center
Keywords: Frailty models; H-likelihood; Joint models; Longitudinal data; Random effects; Time-to-event data

In many clinical studies, multiple longitudinal non-survival responses are recorded simultaneously with a single event time or multiple competing event responses on each study subject. Naturally, these two types of outcomes may be correlated because they are observed from the same individual. To understand this association, a common approach is to model jointly hierarchical generalized linear models (HGLM) and frailty models (unobserved random effects). We propose a joint parametric modeling framework that accounts for the intrinsic association via shared random effects. A single parameter Weibull distribution, whose distribution is appropriate for the analysis of datasets with fewer events, is used. We relax the normality assumption on the distribution of the shared random effects. For inference, the hierarchical likelihood (h-likelihood), which is an efficient fitting procedure, is used to avoid complex integrations. A numerical study is conducted to illustrate the performance of the proposed method and a data example is shown.

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

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