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The problem of dynamic prediction with time-dependent covariates, given bybiomarkers, repeatedly measured over time, has received much attention over thelast decades. Two contrasting approaches have become in widespread use. Thefirst is joint modelling, which attempts to jointly model the longitudinalmarkers and the event time. The second is landmarking, a more pragmaticapproach that avoids modelling the marker process. Landmarking has been shownto be less efficient than correctly specified joint models in simulationstudies, when data are generated from the joint model. When the mean model ismisspecified, however, simulation has shown that joint models may be inferiorto landmarking.  The objective of this paper is to develop methods that improve the predictiveaccuracy of landmarking, while retaining its relative simplicity androbustness. We start by fitting a working longitudinal model for the biomarker,including a temporal correlation structure. Based on that model, we derive apredictable time-dependent process representing the expected value of thebiomarker after the landmark time, and we fit a time-dependent Cox model basedon the predictable time-dependent covariate. Dynamic predictions based on thisapproach for new patients can be obtained by first deriving the expected valuesof the biomarker, given the measured values before the landmark time point, andthen calculating the predicted probabilities based on the time-dependent Coxmodel.  We illustrate the approach in predicting overall survival in liver cirrhosispatients based on prothrombin index.

Landmarking 2.0: Bridging the gap between joint models and landmarking