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Activity Number: 307 - Advanced Survival Analysis Tools for Statistical Learning from Complex Scientific Studies
Type: Topic Contributed
Date/Time: Wednesday, August 5, 2020 : 10:00 AM to 11:50 AM
Sponsor: Lifetime Data Science Section
Abstract #309706
Title: Flexible Semiparametric Latent Class Analysis of Recurrent Events Outcomes
Author(s): Limin Peng* and Wei Zhao and John Hanfelt
Companies: Emory University and Emory University and Emory University
Keywords: Recurrent events; latent class; frailty; estimating equation

Recurrent events data collected from clinical follow-up studies contain rich information on the evolvement of survival phenotype over time. Latent class analysis is a useful venue to parsimoniously characterize the heterogeneity in survival phenotype trajectories across the study population of interest. In this work, we propose a new semi-parametric model that sensibly accounts for potential latent subgroups with different recurrent event risk profiles. We develop a robust estimation procedure from adapting the idea of artificial estimating equation technique. We provide rigorous theoretical justifications for the proposed estimators. Our simulation studies suggest good finite-sample performance of the proposed method as well as dramatic gains over benchmarks without accommodating latent classes. We also apply our method to an Alzheimer dataset.

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

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