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Activity Number:
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470
- Lifetime Risk, Competing Risk, and Recurrent Events
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Type:
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Contributed
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Date/Time:
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Thursday, August 6, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Lifetime Data Science Section
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Abstract #312955
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Title:
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Bayesian Models for Joint Longitudinal and Competing Risks Data
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Author(s):
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Allison Furgal* and Ananda Sen and Jeremy Taylor
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Companies:
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University of Michigan Biostatistics and University of Michigan Biostatistics and University of Michigan
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Keywords:
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joint model;
competing risks;
Bayesian;
longitudinal;
latent failure time
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Abstract:
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Joint models are useful in analyzing data with a survival time and an associated longitudinal marker. When the survival outcome can have multiple causes, competing risks techniques must be incorporated in the joint model. Research in joint modeling of longitudinal and competing risks data has almost exclusively used cause-specific hazard functions. Such modeling is unable to capture the explicit effect of the association between the risk components and the longitudinal marker. We explore Bayesian joint model within a latent failure time framework using parametric models based on a multivariate Weibull and log-Normal distributions. Flexibility is added through nonparametric Dirichlet process priors. We evaluate our model via simulations. We illustrate the approach with an application to data from adrenocortical carcinoma patients at the University of Michigan measuring morphomic markers of body composition over time as well as time to cancer progression or death.
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Authors who are presenting talks have a * after their name.
