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

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

SUBMITTER: Suresh K 

PROVIDER: S-EPMC5957493 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature