Abstract
Longitudinal data are often available in cohort studies and clinical settings, such as covariates collected at cohort follow-upvisits or prescriptions captured in electronic health records. Such longitudinal information, if correlates with the healthevent of interest, may be incorporated to dynamically predict the probability of a health event with better precision.Landmarking is a popular approach to dynamic prediction. There are well-established methods for landmarking usingfull cohort data, but collecting data on all cohort members may not be feasible when resource is limited. Instead,one may select a subset of the cohort using subsampling designs, and only collect data on this subset. In this work,we present conditional likelihood and inverse-probability weighted methods for landmarking using data from cohortsubsampling designs, and discuss considerations for choosing a particular method. Simulations are conducted to evaluatethe applicability of the methods and their predictive performance in different scenarios. Results show that our methodshave similar predictive performance to the full cohort analysis but only use small fractions of the full cohort data. Weuse real nested case-control data from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial toillustrate the methods

http://dx.doi.org/10.1177/09622802251403279