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

  title={Tutorial in Joint Modeling and Prediction: a Statistical Software for Correlated Longitudinal Outcomes, Recurrent Events and a Terminal Event},
  author={Agnieszka Kr'ol and Audrey Mauguen and Yassin Mazroui and Alexandre Laurent and Stefan Michiels and Virginie Rondeau},
  journal={arXiv: Computation},
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… 

A tutorial for joint modeling of longitudinal and time-to-event data in R

This tutorial presents an overview and general framework for joint modeling of longitudinal and time-to-event data, and fully illustrates its application in the context of a behavioral (cognitive aging) study.

joineRML: a joint model and software package for time-to-event and multivariate longitudinal outcomes

This work describes the classical joint model to the case of multiple longitudinal outcomes, proposes a practical algorithm for fitting the models, and demonstrates how to fit the models using a new package for the statistical software platform R, joineRML.

Bayesian joint modelling of longitudinal and time to event data: a methodological review

A comprehensive review on Bayesian univariate and multivariate joint models has been undertaken, finding joint modelling has been proved to be beneficial in producing more accurate dynamic prediction; however, there is a lack of sufficient tools to validate the prediction.

Design and analysis of nested case–control studies for recurrent events subject to a terminal event

This work develops a general framework for the design of NCC studies in the presence of recurrent and terminal events and proposes estimation and inference for a joint frailty model for recurrence and death using data arising from such studies.

Prediction of Time to a Terminal Event (TTTE) of New Units in a Dynamic Recurrent Competing Risks Model

A simulation approach to predict time to terminal event (TE) times that arises from joint dynamic modelling and points out the size-biased sampling related to gap time that traverses monitoring time.

Bayesian Joint Models for Longitudinal and Multi-state Survival Data

A semi-Markov process that consider the time spent in the current state is defined for the transitions between states and a Bayesian method using Markov Chain Monte Carlo (MCMC) is developed for parameter estimation and inferences.

Joint Modeling of Multivariate Survival Data With an Application to Retirement

The Cox proportional hazards model has been pervasively used in many social science areas to examine the effects of covariates on timing to an event. The standard Cox model is intended to study

Bayesian Inference in a Joint Model for Longitudinal and Time to Event Data with Gompertz Baseline Hazards

An alternative joint model approach under Bayesian prospective is proposed to determine the association between markers(tumor sizes) and time to death among cancer patients without recurrence and it is suggested that the proposed joint modeling approach perform well in terms of parameter estimations when correlation between random intercepts and slopes is considered.

Semi-Parametric Joint Modeling of Survival and Longitudinal Data: The R Package JSM

This paper presents the joint modeling framework that is implemented in JSM, as well as the standard error estimation methods, and illustrates the package with two real data examples: a liver cirrhosis data and a Mayo Clinic primary biliary cirrhotic data.

A Joint Model for Longitudinal and Time-to-event Data in Social and Life Course Research: Employment Status and Time to Retirement

Longitudinal studies including a time-to-event outcome in social research often use a form of event history analysis to analyse the influence of time-varying endogenous covariates on the



Multivariate frailty models for two types of recurrent events with a dependent terminal event: Application to breast cancer data

A multivariate frailty model is proposed that jointly analyzes two types of recurrent events with a dependent terminal event and the terminal event (death) after a breast cancer.

Joint latent class models for longitudinal and time-to-event data: A review

This article aims at giving an overview of joint latent class modelling, especially in the prediction context, by introducing the model, discussing estimation and goodness-of-fit, and comparing it with the shared random-effect model.

Shared Frailty Models for Recurrent Events and a Terminal Event

This model avoids the difficulties encountered in alternative approaches which attempt to specify a dependent joint distribution with marginal proportional hazards and yields an estimate of the degree of dependence.

A Joint Model for Longitudinal Measurements and Survival Data in the Presence of Multiple Failure Types

This joint model provides a flexible approach to handle possible nonignorable missing data in the longitudinal measurements due to dropout and is an extension of previous joint models with a single failure type, offering a possible way to model informatively censored events as a competing risk.

Joint frailty models for recurring events and death using maximum penalized likelihood estimation: application on cancer events.

It is shown how maximum penalized likelihood estimation can be applied to nonparametric estimation of the continuous hazard functions in a general joint frailty model with right censoring and delayed entry, and yields satisfactory results in this complex setting.

Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time‐to‐Event Data

This article presents how survival probabilities can be estimated for future subjects based on their available longitudinal measurements and a fitted joint model and assesses how well the marker is capable of discriminating between subjects who experience the event within a medically meaningful time frame from subjects who do not.

Multilevel Bayesian Models for Survival Times and Longitudinal Patient-Reported Outcomes With Many Zeros

This work contributes to understanding the impact of treatment on two aspects of mesothelioma: patients’ subjective experience of the disease process and their progression-free survival times.

Joint Models of Longitudinal Data and Recurrent Events with Informative Terminal Event

This article presents semiparametric joint models to analyze longitudinal data with recurrent events and a terminal event such as death and proposes to estimate all the parameters using the nonparametric maximum likelihood estimators (NPMLE).

Joint Models for Longitudinal and Time-to-Event Data: With Applications in R

This paper presents a meta-analysis of longitudinal and time-to-Event data patterns and discusses the role of the Parameterization on Predictions and Prospective Accuracy Measures for Longitudinal Markers in predicting survival and longitudinal outcomes.

frailtypack: A computer program for the analysis of correlated failure time data using penalized likelihood estimation