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.
Highlights
Joint modelsRecent technologies allow registration of greater and greater amount of data
The standard survival analysis for overall survival (OS) may lead to biased estimations, if the repeated data is considered as time-varying covariates or if it is completely ignored in the analysis
In the package frailtypack we provide prediction function for dynamic predictions of a terminal event in a finite horizon, epoce function for evaluating predictive accuracy of a joint model and Diffepoce for comparing the accuracy of two joint models
Summary
Recent technologies allow registration of greater and greater amount of data. In the medical research different kinds of patient information are gathered over time together with clinical outcome data such as overall survival (OS). A review of the joint modeling of longitudinal and survival data was already given elsewhere (McCrink, Marshall, and Cairns 2013; Lawrence Gould, Boye, Crowther, Ibrahim, Quartey, Micallef, and Bois 2014; Asar, Ritchie, Kalra, and Diggle 2015) Another option for the analysis of correlated data are joint models for recurrent events and a terminal event (Liu, Wolfe, and Huang 2004; Rondeau, Mathoulin-Pelissier, Jacqmin-Gadda, Brouste, and Soubeyran 2007). A relationship between longitudinal outcomes and Agnieszka Krol, Audrey Mauguen, Yassin Mazroui, Alexandre Laurent, Stefan Michiels, Virginie Ro3ndeau recurrent events was not considered This relationship was incorporated to a trivariate model proposed by Liu and Huang (2009) for an application of an HIV study.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
