Tutorial in Joint Modeling and Prediction: a Statistical Software for Correlated Longitudinal Outcomes, Recurrent Events and a Terminal Event

TL;DR

This paper introduces the R package frailtypack, which implements joint models for correlated longitudinal and survival data, enabling prognosis research through estimation, goodness-of-fit, and dynamic prediction.

Contribution

It provides a comprehensive software tool with new models and estimation techniques for joint modeling of complex correlated data structures.

Findings

Provides estimators using maximum likelihood methods.

Includes functions for goodness-of-fit and baseline hazard plots.

Enables individual dynamic predictions of terminal events.

Abstract

Extensions in the field of joint modeling of correlated data and dynamic predictions improve the development of prognosis research. The R package frailtypack provides estimations of various joint models for longitudinal data and survival events. In particular, it fits models for recurrent events and a terminal event (frailtyPenal), models for two survival outcomes for clustered data (frailtyPenal), models for two types of recurrent events and a terminal event (multivPenal), models for a longitudinal biomarker and a terminal event (longiPenal) and models for a longitudinal biomarker, recurrent events and a terminal event (trivPenal). The estimators are obtained using a standard and penalized maximum likelihood approach, each model function allows to evaluate goodness-of-fit analyses and plots of baseline hazard functions. Finally, the package provides individual dynamic predictions of the terminal event and evaluation of predictive accuracy. This paper presents theoretical models with estimation techniques, applies the methods for predictions and illustrates frailtypack functions details with examples.