Joint frailty models for zero-inflated recurrent events in the presence of a terminal event

Biometrics ◽  
2015 ◽  
Vol 72 (1) ◽  
pp. 204-214 ◽  
Author(s):  
Lei Liu ◽  
Xuelin Huang ◽  
Alex Yaroshinsky ◽  
Janice N. Cormier
Keyword(s):  
Recurrent Events ◽  
Frailty Models ◽  
Terminal Event

scholarly journals Shared Frailty Models for Recurrent Events and a Terminal Event

Biometrics ◽  
2004 ◽  
Vol 60 (3) ◽  
pp. 747-756 ◽  
Author(s):  
Lei Liu ◽  
Robert A. Wolfe ◽  
Xuelin Huang
Keyword(s):  
Recurrent Events ◽  
Frailty Models ◽  
Terminal Event ◽  
Shared Frailty

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

2013 ◽  
Vol 55 (6) ◽  
pp. 866-884 ◽  
Author(s):  
Yassin Mazroui ◽  
Simone Mathoulin-Pélissier ◽  
Gaetan MacGrogan ◽  
Véronique Brouste ◽  
Virginie Rondeau
Keyword(s):  
Breast Cancer ◽  
Recurrent Events ◽  
Frailty Models ◽  
Terminal Event ◽  
Breast Cancer Data ◽  
Cancer Data

Ascertainment correction in frailty models for recurrent events data

2016 ◽  
Vol 35 (23) ◽  
pp. 4183-4201
Author(s):  
Theodor A. Balan ◽  
Marianne A. Jonker ◽  
Paul C. Johannesma ◽  
Hein Putter
Keyword(s):  
Recurrent Events ◽  
Frailty Models ◽  
Recurrent Events Data ◽  
Events Data ◽  
Ascertainment Correction

Joint model for left-censored longitudinal data, recurrent events and terminal event: Predictive abilities of tumor burden for cancer evolution with application to the FFCD 2000-05 trial

Biometrics ◽  
2016 ◽  
Vol 72 (3) ◽  
pp. 907-916 ◽  
Author(s):  
Agnieszka Król ◽  
Loïc Ferrer ◽  
Jean-Pierre Pignon ◽  
Cécile Proust-Lima ◽  
Michel Ducreux ◽  
...  
Keyword(s):  
Longitudinal Data ◽  
Recurrent Events ◽  
Joint Model ◽  
Tumor Burden ◽  
Terminal Event ◽  
Cancer Evolution

Modeling marginal features in studies of recurrent events in the presence of a terminal event

2019 ◽  
Vol 25 (4) ◽  
pp. 681-695 ◽  
Author(s):  
Per Kragh Andersen ◽  
Jules Angst ◽  
Henrik Ravn
Keyword(s):  
Recurrent Events ◽  
Terminal Event

scholarly journals A tutorial on frailty models

2020 ◽  
Vol 29 (11) ◽  
pp. 3424-3454 ◽  
Author(s):  
Theodor A Balan ◽  
Hein Putter
Keyword(s):  
Random Effects ◽  
Recurrent Events ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Unobserved Heterogeneity ◽  
Frailty Models ◽  
Survival Outcomes ◽  
Time To Event ◽  
Homogeneous Population ◽  
Hazards Model

The hazard function plays a central role in survival analysis. In a homogeneous population, the distribution of the time to event, described by the hazard, is the same for each individual. Heterogeneity in the distributions can be accounted for by including covariates in a model for the hazard, for instance a proportional hazards model. In this model, individuals with the same value of the covariates will have the same distribution. It is natural to think that not all covariates that are thought to influence the distribution of the survival outcome are included in the model. This implies that there is unobserved heterogeneity; individuals with the same value of the covariates may have different distributions. One way of accounting for this unobserved heterogeneity is to include random effects in the model. In the context of hazard models for time to event outcomes, such random effects are called frailties, and the resulting models are called frailty models. In this tutorial, we study frailty models for survival outcomes. We illustrate how frailties induce selection of healthier individuals among survivors, and show how shared frailties can be used to model positively dependent survival outcomes in clustered data. The Laplace transform of the frailty distribution plays a central role in relating the hazards, conditional on the frailty, to hazards and survival functions observed in a population. Available software, mainly in R, will be discussed, and the use of frailty models is illustrated in two different applications, one on center effects and the other on recurrent events.

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