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Activity Number: 83 - SPEED: Survival Analysis
Type: Contributed
Date/Time: Sunday, July 29, 2018 : 4:00 PM to 4:45 PM
Sponsor: Biometrics Section
Abstract #332719
Title: A Gaussian Copula Approach for Dynamic Prediction of Survival with a Longitudinally Measured Marker
Author(s): Krithika Suresh* and Jeremy M.G. Taylor and Alexander Tsodikov
Companies: University of Michigan and University of Michigan and University of Michigan
Keywords: Dynamic prediction; Joint modeling; Landmarking; Longitudinal marker; Survival

Dynamic prediction uses patient information collected during follow-up to produce individualized survival predictions at given time points beyond treatment or diagnosis. 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 restrictive distributional assumptions and computational intensive methods for estimation. We introduce an alternative approximate approach for dynamic prediction that aims to overcome the limitations of both methods, while achieving good predictive performance. We propose using a Gaussian copula to model the association between a failure time outcome and longitudinal marker data. We specify the marker and failure time distributions conditional on surviving up to a prediction time of interest, and use the copula to link them smoothly at each prediction time using an association function. Estimation is conducted using a two-stage approach. We compare the predictive performance of our model to existing methods using a simulation study and a real data application.

Authors who are presenting talks have a * after their name.

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