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Activity Number:
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275
- Joint Models for Complex Data: An Update on Computational Issues, Solutions, and Applications
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Type:
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Topic-Contributed
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Date/Time:
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Wednesday, August 11, 2021 : 1:30 PM to 3:20 PM
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Sponsor:
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International Chinese Statistical Association
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Abstract #317546
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Title:
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A Variable Selection Method for the Joint Model of Longitudinal and Survival Data with Its Application in Clinical Data Analysis
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Author(s):
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Tao Wang*
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Companies:
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Yunnan Normal University
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Keywords:
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variable selection;
joint model;
longitudinal data;
survival data;
spike-and-slab lasso
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Abstract:
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Although there has been extensive research for joint modelling method of longitudinal and survival data in the last two decades motivated by the requirements of increasingly application and the importance of such joint models has been increasingly recognized, but the research on variable selection method for joint models of longitudinal and survival outcomes with lower computational load is still getting on slowly. We propose a novel Bayesian variable selection method based on spike-and-slab lasso for semi-parametric joint model which consists of a semi-parametric mixed effects model for longitudinal data and a semi-parametric Cox proportional hazards model for survival data linked through shared random effects. We develop the computational program for such variable selection method. Simulation studies and real data analysis demonstrate that our method performs well.
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Authors who are presenting talks have a * after their name.
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