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Activity Number:
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408
- Joint Modeling of Longitudinal and Survival Data and Related Topics
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Type:
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Contributed
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Date/Time:
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Tuesday, July 30, 2019 : 2:00 PM to 3:50 PM
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Sponsor:
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Lifetime Data Science Section
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Abstract #306801
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Presentation
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Title:
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A Gaussian Copula Approach for Dynamic Prediction of Survival with a Longitudinal Biomarker
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Author(s):
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Krithika Suresh* and Jeremy Taylor and Alexander Tsodikov
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Companies:
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University of Colorado and University of Michigan and University of Michigan
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Keywords:
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dynamic prediction;
Gaussian copula;
landmarking;
joint modeling;
survival analysis;
longitudinal data
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Abstract:
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Dynamic prediction uses patient information collected during follow-up to produce individualized survival predictions at given time points beyond baseline. This allows clinicians to obtain updated predictions of a patient's prognosis that can be used in making personalized treatment decisions. Two commonly used approaches for dynamic prediction are landmarking and joint modeling. Landmarking does not constitute a comprehensive probability model, and joint modeling often requires strong distributional assumptions. We introduce an alternative approach that aims to overcome the limitations of both methods. We separately specify the marker and failure time distributions conditional on surviving up to a prediction time of interest and use standard goodness-of-fit techniques to identify the best-fitting models. Taking advantage of its analytic tractability and easy two-stage estimation, we use a Gaussian copula to link the marginal distributions smoothly at each prediction time with an association function. We illustrate the utility of our method in an application to predicting death for heart valve transplant patients using longitudinal left ventricular mass index (LVMI) information.
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Authors who are presenting talks have a * after their name.
