Penalized regression calibration: A method for the prediction of survival outcomes using complex longitudinal and high-dimensional data.

ABSTRACT: Longitudinal and high-dimensional measurements have become increasingly common in biomedical research. However, methods to predict survival outcomes using covariates that are both longitudinal and high-dimensional are currently missing. In this article, we propose penalized regression calibration (PRC), a method that can be employed to predict survival in such situations. PRC comprises three modeling steps: First, the trajectories described by the longitudinal predictors are flexibly modeled through the specification of multivariate mixed effects models. Second, subject-specific summaries of the longitudinal trajectories are derived from the fitted mixed models. Third, the time to event outcome is predicted using the subject-specific summaries as covariates in a penalized Cox model. To ens

SUBMITTER: Signorelli M 

PROVIDER: S-EPMC9293191 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature