Dynamic Prediction by Landmarking in Event History Analysis

Abstract. This article advocates the landmarking approach that dynamically adjusts predictive models for survival data during the follow up. This updating is achieved by directly fitting models for the individuals still at risk at the landmark point. Using this approach, simple proportional hazards models are able to catch the development over time for models with time‐varying effects of covariates or data with time‐dependent covariates (biomarkers). To smooth the effect of the landmarking, sequences of models are considered with parametric effects of the landmark time point and fitted by maximizing appropriate pseudo log‐likelihoods that extend the partial log‐likelihood to cover the landmarking approach.