Comparison of joint modeling and landmarking for dynamic prediction under an illness-death model

Biometrical Journal. Biometrische Zeitschrift
Krithika SureshAlexander Tsodikov

Abstract

Dynamic prediction incorporates time-dependent marker information accrued during follow-up to improve personalized survival prediction probabilities. At any follow-up, or "landmark", time, the residual time distribution for an individual, conditional on their updated marker values, can be used to produce a dynamic prediction. To satisfy a consistency condition that links dynamic predictions at different time points, the residual time distribution must follow from a prediction function that models the joint distribution of the marker process and time to failure, such as a joint model. To circumvent the assumptions and computational burden associated with a joint model, approximate methods for dynamic prediction have been proposed. One such method is landmarking, which fits a Cox model at a sequence of landmark times, and thus is not a comprehensive probability model of the marker process and the event time. Considering an illness-death model, we derive the residual time distribution and demonstrate that the structure of the Cox model baseline hazard and covariate effects under the landmarking approach do not have simple form. We suggest some extensions of the landmark Cox model that should provide a better approximation. We comp...Continue Reading

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Citations

Nov 2, 2019·Statistics in Medicine·Grigorios PapageorgiouDimitris Rizopoulos
Dec 17, 2019·JCO Precision Oncology·Susan HalabiSheng Luo
Jul 17, 2020·Diagnostic and Prognostic Research·Lucy M BullJamie C Sergeant
Nov 23, 2018·Statistical Methods in Medical Research·Loïc FerrerCécile Proust-Lima
Dec 11, 2019·Biostatistics·Krithika SureshAlexander Tsodikov
Oct 13, 2020·Statistics in Medicine·Per Kragh AndersenTerry M Therneau
Mar 18, 2021·International Journal of Epidemiology·Eleni-Rosalina AndrinopoulouDimitris Rizopoulos
Jun 15, 2021·Statistics in Medicine·Krithika SureshAlexander Tsodikov

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