Literature DB >> 26295794

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

Lei Liu1, Xuelin Huang2, Alex Yaroshinsky3, Janice N Cormier4.   

Abstract

Recurrent event data arise frequently in longitudinal medical studies. In many situations, there are a large portion of subjects without any recurrent events, manifesting the "zero-inflated" nature of the data. Some of the zero events may be "structural zeros" as patients are unsusceptible to recurrent events, while others are "random zeros" due to censoring before any recurrent events. On the other hand, there often exists a terminal event which may be correlated with the recurrent events. In this article, we propose two joint frailty models for zero-inflated recurrent events in the presence of a terminal event, combining a logistic model for "structural zero" status (Yes/No) and a joint frailty proportional hazards model for recurrent and terminal event times. The models can be fitted conveniently in SAS Proc NLMIXED. We apply the methods to model recurrent opportunistic diseases in the presence of death in an AIDS study, and tumor recurrences and a terminal event in a sarcoma study.
© 2015, The International Biometric Society.

Entities:  

Keywords:  Cure model; Finite Mixture; Latent Class; Survival analysis

Mesh:

Year:  2015        PMID: 26295794     DOI: 10.1111/biom.12376

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  9 in total

1.  Exploring causality mechanism in the joint analysis of longitudinal and survival data.

Authors:  Lei Liu; Cheng Zheng; Joseph Kang
Journal:  Stat Med       Date:  2018-06-07       Impact factor: 2.373

2.  A joint model for recurrent events and a semi-competing risk in the presence of multi-level clustering.

Authors:  Tae Hyun Jung; Peter Peduzzi; Heather Allore; Tassos C Kyriakides; Denise Esserman
Journal:  Stat Methods Med Res       Date:  2018-07-31       Impact factor: 3.021

3.  Variable selection in joint frailty models of recurrent and terminal events.

Authors:  Dongxiao Han; Xiaogang Su; Liuquan Sun; Zhou Zhang; Lei Liu
Journal:  Biometrics       Date:  2020-03-03       Impact factor: 2.571

4.  Joint modeling of recurrent events and a terminal event adjusted for zero inflation and a matched design.

Authors:  Cong Xu; Vernon M Chinchilli; Ming Wang
Journal:  Stat Med       Date:  2018-04-22       Impact factor: 2.373

5.  Compound Poisson frailty model with a gamma process prior for the baseline hazard: accounting for a cured fraction.

Authors:  Maryam Rahmati; Parisa Rezanejad Asl; Javad Mikaeli; Hojjat Zeraati; Aliakbar Rasekhi
Journal:  J Appl Stat       Date:  2021-07-05       Impact factor: 1.416

6.  Joint model for bivariate zero-inflated recurrent event data with terminal events.

Authors:  Yang-Jin Kim
Journal:  J Appl Stat       Date:  2020-03-24       Impact factor: 1.416

7.  Efficient Multiple Imputation for Sensitivity Analysis of Recurrent Events Data with Informative Censoring.

Authors:  Guoqing Diao; Guanghan F Liu; Donglin Zeng; Yilong Zhang; Gregory Golm; Joseph F Heyse; Joseph G Ibrahim
Journal:  Stat Biopharm Res       Date:  2020-11-05       Impact factor: 1.586

8.  Regression analysis of mixed panel-count data with application to cancer studies.

Authors:  Yimei Li; Liang Zhu; Lei Liu; Leslie L Robison
Journal:  Stat Biosci       Date:  2020-08-17

9.  Joint modeling of longitudinal data with informative cluster size adjusted for zero-inflation and a dependent terminal event.

Authors:  Biyi Shen; Chixiang Chen; Danping Liu; Somnath Datta; Nasrollah Ghahramani; Vernon M Chinchilli; Ming Wang
Journal:  Stat Med       Date:  2021-05-31       Impact factor: 2.373

  9 in total

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