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.

翻译：在生存分析中，患者的纵向健康信息可用于动态更新其发生感兴趣事件的预测概率。传统的动态预测方法（如联合模型）在处理少量纵向协变量时即面临计算可行性问题，亟需开发能处理大量纵向协变量的方法。本文介绍R包pencal，其实现了惩罚回归校准方法，可处理众多纵向协变量作为生存预测因子。pencal使用混合效应模型总结纵向协变量在预先指定标志性时间点的轨迹特征，并采用惩罚Cox模型基于基线协变量与纵向协变量汇总指标进行生存预测。本文阐述了该R包的结构，通过分步骤示例演示了如何估计PRC、计算生存动态预测及验证模型性能，并展示了如何通过并行化显著缩短计算时间。