Literature DB >> 27761797

Regression analysis of clustered failure time data with informative cluster size under the additive transformation models.

Ling Chen1, Yanqin Feng2,3, Jianguo Sun4.   

Abstract

This paper discusses regression analysis of clustered failure time data, which occur when the failure times of interest are collected from clusters. In particular, we consider the situation where the correlated failure times of interest may be related to cluster sizes. For inference, we present two estimation procedures, the weighted estimating equation-based method and the within-cluster resampling-based method, when the correlated failure times of interest arise from a class of additive transformation models. The former makes use of the inverse of cluster sizes as weights in the estimating equations, while the latter can be easily implemented by using the existing software packages for right-censored failure time data. An extensive simulation study is conducted and indicates that the proposed approaches work well in both the situations with and without informative cluster size. They are applied to a dental study that motivated this study.

Keywords:  Additive transformation model; Informative cluster size; Weighted estimating equation; Within-cluster resampling

Mesh:

Year:  2016        PMID: 27761797     DOI: 10.1007/s10985-016-9384-x

Source DB:  PubMed          Journal:  Lifetime Data Anal        ISSN: 1380-7870            Impact factor:   1.588


  5 in total

1.  Marginal analysis of correlated failure time data with informative cluster sizes.

Authors:  Xiuyu J Cong; Guosheng Yin; Yu Shen
Journal:  Biometrics       Date:  2007-09       Impact factor: 2.571

2.  Modeling survival data with informative cluster size.

Authors:  John M Williamson; Hae-Young Kim; Amita Manatunga; David G Addiss
Journal:  Stat Med       Date:  2008-02-20       Impact factor: 2.373

3.  Incorporating correlation for multivariate failure time data when cluster size is large.

Authors:  L Xue; L Wang; A Qu
Journal:  Biometrics       Date:  2009-08-10       Impact factor: 2.571

4.  Additive transformation models for clustered failure time data.

Authors:  Donglin Zeng; Jianwen Cai
Journal:  Lifetime Data Anal       Date:  2009-12-11       Impact factor: 1.588

5.  Prognosis versus actual outcome. III. The effectiveness of clinical parameters in accurately predicting tooth survival.

Authors:  M K McGuire; M E Nunn
Journal:  J Periodontol       Date:  1996-07       Impact factor: 6.993

  5 in total

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