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Activity Number:
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100
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Type:
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Invited
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Date/Time:
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Monday, August 1, 2016 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics in Epidemiology
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Abstract #318227
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View Presentation
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Title:
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A Flexible Joint Longitudinal-Survival Model for Quantifying the Association Between Serum Biomarkers and Mortality
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Author(s):
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Daniel Gillen* and Sepehr Arkhavan and Babak Shahbaba
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Companies:
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University of California at Irvine and University of California at Irvine and University of California at Irvine
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Keywords:
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survival ;
longitudinal ;
biomarker ;
gaussian process ;
volatility ;
ESRD
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Abstract:
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We propose a joint longitudinal-survival model for associating summary measures of a longitudinally collected biomarker with a time-to-event endpoint. The model is robust to common parametric and semi-parametric assumptions in that it avoids simple distributional assumptions on longitudinal measures and allows for associating novel functionals of the biomarker distribution with the risk of mortality. Specifically, we use a Gaussian process model with a parameter that captures within-subject volatility in the longitudinally sampled biomarker, where the unknown distribution of the parameter is assumed to have a Dirichlet process prior. We then estimate the association between within-subject volatility and the risk of mortality using a flexible survival model. Joint estimation is performed to account for uncertainty in the estimated within-subject volatility measure. Simulation studies are presented to assess the operating characteristics of the proposed model. Using data from the United States Renal Data System we quantify the association between within-subject volatility in serum albumin and the risk of mortality among ESRD patients.
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Authors who are presenting talks have a * after their name.
