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