Title: A Gaussian copula approach for dynamic prediction of survival with a longitudinal biomarker
Authors: Krithika Suresh - University of Colorado (United States) [presenting]
Jeremy Taylor - University of Michigan (United States)
Alexander Tsodikov - University of Michigan (United States)
Abstract: Dynamic prediction uses patient information collected during follow-up to produce individualized survival predictions at given time points beyond treatment or diagnosis. 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 and computationally 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 separately specify the marker and failure time distributions conditional on surviving up to a prediction time of interest and use standard variable selection and goodness-of-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 using an association function. With simulation studies, we examine the proposed method's performance and identify situations where it is preferable over existing approaches. We illustrate its advantages for dynamic prediction in an application to health data.