Literature DB >> 11315010

A marginal mixed baseline hazards model for multivariate failure time data.

L X Clegg1, J Cai, P K Sen.   

Abstract

In multivariate failure time data analysis, a marginal regression modeling approach is often preferred to avoid assumptions on the dependence structure among correlated failure times. In this paper, a marginal mixed baseline hazards model is introduced. Estimating equations are proposed for the estimation of the marginal hazard ratio parameters. The proposed estimators are shown to be consistent and asymptotically Gaussian with a robust covariance matrix that can be consistently estimated. Simulation studies indicate the adequacy of the proposed methodology for practical sample sizes. The methodology is illustrated with a data set from the Framingham Heart Study.

Mesh:

Year:  1999        PMID: 11315010     DOI: 10.1111/j.0006-341x.1999.00805.x

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


  9 in total

1.  Hierarchical dependency models for multivariate survival data with censoring.

Authors:  S Gross; C Huber
Journal:  Lifetime Data Anal       Date:  2000-12       Impact factor: 1.588

2.  Marginal means/rates models for multiple type recurrent event data.

Authors:  Jianwen Cai; Douglas E Schaubel
Journal:  Lifetime Data Anal       Date:  2004-06       Impact factor: 1.588

3.  Variable selection for multivariate failure time data.

Authors:  Jianwen Cai; Jianqing Fan; Runze Li; Haibo Zhou
Journal:  Biometrika       Date:  2005       Impact factor: 2.445

4.  Marginal hazards regression for retrospective studies within cohort with possibly correlated failure time data.

Authors:  Sangwook Kang; Jianwen Cai
Journal:  Biometrics       Date:  2008-05-19       Impact factor: 2.571

5.  Regression analysis of multivariate recurrent event data with a dependent terminal event.

Authors:  Liang Zhu; Jianguo Sun; Xingwei Tong; Deo Kumar Srivastava
Journal:  Lifetime Data Anal       Date:  2010-03-10       Impact factor: 1.588

6.  Semiparametric methods for clustered recurrent event data.

Authors:  Douglas E Schaubel; Jianwen Cai
Journal:  Lifetime Data Anal       Date:  2005-09       Impact factor: 1.588

7.  Estimating the ratio of multivariate recurrent event rates with application to a blood transfusion study.

Authors:  Jing Ning; Mohammad H Rahbar; Sangbum Choi; Jin Piao; Chuan Hong; Deborah J Del Junco; Elaheh Rahbar; Erin E Fox; John B Holcomb; Mei-Cheng Wang
Journal:  Stat Methods Med Res       Date:  2015-07-09       Impact factor: 3.021

8.  Non-parametric regression in clustered multistate current status data with informative cluster size.

Authors:  Ling Lan; Dipankar Bandyopadhyay; Somnath Datta
Journal:  Stat Neerl       Date:  2016-10-25       Impact factor: 1.190

9.  ANALYSIS OF MULTIVARIATE FAILURE TIME DATA USING MARGINAL PROPORTIONAL HAZARDS MODEL.

Authors:  Ying Chen; Kani Chen; Zhiliang Ying
Journal:  Stat Sin       Date:  2010       Impact factor: 1.261

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.