Biostatistics Weekly Seminar

Dynamic Prediction of Survival with a Longitudinally Measured Marker

Krithika Suresh, PhD
Research Assistant Professor, Department of Biostatistics and Informatics
Colorado School of Public Health

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. In this talk, we begin by describing two common approaches for dynamic prediction: joint modeling and landmarking. We then propose a novel alternative approach that aims to overcome some of the limitations of the existing methods. Using a Gaussian copula, we specify the joint distribution of a longitudinal marker and failure time conditional on surviving to a prediction time of interest. We illustrate the utility of our method in an application to predicting death for heart valve transplant patients using longitudinal left ventricular mass index information. We describe extensions of this approach to settings with a binary marker.

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04 May 2022

Speaker Itinerary

Topic revision: r1 - 26 Apr 2022, JenaAltstatt

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