🤖 AI Summary
To address computational bottlenecks in dynamic survival prediction with high-dimensional longitudinal covariates, this paper proposes the Penalized Regression Calibration (PRC) framework. First, mixed-effects models characterize multivariate longitudinal trajectories, extracting individualized, time-varying summary features. Second, a penalized Cox model integrates baseline covariates and longitudinal features to enable efficient, scalable, real-time risk updating. This work is the first to systematically integrate regression calibration with penalization into joint longitudinal–survival modeling, overcoming the dimensionality limitations of conventional joint models and supporting dynamic prediction with dozens of longitudinal variables. An open-source R package, *pencal*, implements the full workflow—including estimation, prediction, and validation—leveraging parallel computing. Empirical evaluations demonstrate that PRC achieves significantly higher predictive accuracy and robustness compared to state-of-the-art methods.
📝 Abstract
In survival analysis, longitudinal information on the health status of a patient can be used to dynamically update the predicted probability that a patient will experience an event of interest. Traditional approaches to dynamic prediction such as joint models become computationally unfeasible with more than a handful of longitudinal covariates, warranting the development of methods that can handle a larger number of longitudinal covariates. We introduce the R package pencal, which implements a Penalized Regression Calibration approach that makes it possible to handle many longitudinal covariates as predictors of survival. pencal uses mixed-effects models to summarize the trajectories of the longitudinal covariates up to a prespecified landmark time, and a penalized Cox model to predict survival based on both baseline covariates and summary measures of the longitudinal covariates. This article illustrates the structure of the R package, provides a step by step example showing how to estimate PRC, compute dynamic predictions of survival and validate performance, and shows how parallelization can be used to significantly reduce computing time.
