pencal: an R Package for the Dynamic Prediction of Survival with Many Longitudinal Predictors

TL;DR

The paper introduces pencal, an R package that enables dynamic survival prediction using many longitudinal predictors through a penalized regression approach, overcoming computational limitations of traditional joint models.

Contribution

It presents a novel penalized regression calibration method implemented in an R package to handle numerous longitudinal covariates for survival prediction.

Findings

pencal effectively manages many longitudinal predictors in survival analysis.

The package improves computational efficiency via parallelization.

Demonstrates accurate dynamic survival predictions with real data.

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 (PRC) 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.