Literature DB >> 25419023

Regression analysis of clustered interval-censored failure time data with informative cluster size.

Xinyan Zhang1, Jianguo Sun1.   

Abstract

Correlated or clustered failure time data often occur in medical studies, among other fields (Cai and Prentice, 1995; Kalbfleisch and Prentice, 2002), and sometimes such data arise together with interval censoring (Wang et al., 2006). Furthermore, the failure time of interest may be related to the cluster size. For example, Williamson et al. (2008) discussed such an example arising from a lymphatic filariasis study. A simple and common approach to the analysis of these data is to simplify or convert interval-censored data to right-censored data due to the lack of proper inference procedures for direct analysis of these data. In this paper, two procedures are presented for regression analysis of clustered failure time data that allow both interval censoring and informative cluster size. Simulation studies are conducted to evaluate the presented approaches and they are applied to a motivating example.

Entities:  

Keywords:  Informative cluster size; Interval censoring; Weibull model; Within-cluster resampling

Year:  2010        PMID: 25419023      PMCID: PMC4240509          DOI: 10.1016/j.csda.2010.01.035

Source DB:  PubMed          Journal:  Comput Stat Data Anal        ISSN: 0167-9473            Impact factor:   1.681


  5 in total

1.  A multiple imputation approach to Cox regression with interval-censored data.

Authors:  W Pan
Journal:  Biometrics       Date:  2000-03       Impact factor: 2.571

2.  A goodness-of-fit test for the marginal Cox model for correlated interval-censored failure time data.

Authors:  Lianming Wang; Liuquan Sun; Jianguo Sun
Journal:  Biom J       Date:  2006-12       Impact factor: 2.207

3.  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

4.  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

5.  A Bayesian approach for joint modeling of cluster size and subunit-specific outcomes.

Authors:  David B Dunson; Zhen Chen; Jean Harry
Journal:  Biometrics       Date:  2003-09       Impact factor: 2.571

  5 in total
  1 in total

1.  A multiple imputation approach to the analysis of clustered interval-censored failure time data with the additive hazards model.

Authors:  Ling Chen; Jianguo Sun; Chengjie Xiong
Journal:  Comput Stat Data Anal       Date:  2016-05-28       Impact factor: 1.681

  1 in total

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