Literature DB >> 11878224

Optimal weight functions for marginal proportional hazards analysis of clustered failure time data.

Robert J Gray1, Yi Li.   

Abstract

The choice of weights in estimating equations for multivariate survival data is considered. Specifically, we consider families of weight functions which are constant on fixed time intervals, including the special case of time-constant weights. For a fixed set of time intervals, the optimal weights are identified as the solution to a system of linear equations. The optimal weights are computed for several scenarios. It is found that for the scenarios examined, the gains in efficiency using the optimal weights are quite small relative to simpler approaches except under extreme dependence, and that a simple estimator of an exchangeable approximation to the weights also performs well.

Mesh:

Year:  2002        PMID: 11878224     DOI: 10.1023/a:1013568114539

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


  1 in total

1.  Regression estimation using multivariate failure time data and a common baseline hazard function model.

Authors:  J Cai; R L Prentice
Journal:  Lifetime Data Anal       Date:  1997       Impact factor: 1.588

  1 in total
  5 in total

1.  Weighted estimating equations for linear regression analysis of clustered failure time data.

Authors:  Robert J Gray
Journal:  Lifetime Data Anal       Date:  2003-06       Impact factor: 1.588

Review 2.  Design and analysis of group-randomized trials: a review of recent methodological developments.

Authors:  David M Murray; Sherri P Varnell; Jonathan L Blitstein
Journal:  Am J Public Health       Date:  2004-03       Impact factor: 9.308

3.  Gaining Efficiency via Weighted Estimators for Multivariate Failure Time Data*

Authors:  Jianqing Fan; Yong Zhou; Jianwen Cai; Min Chen
Journal:  Sci China Ser A Math       Date:  2009-06-01

4.  SEMIPARAMETRIC REGRESSION WITH TIME-DEPENDENT COEFFICIENTS FOR FAILURE TIME DATA ANALYSIS.

Authors:  Zhangsheng Yu; Xihong Lin
Journal:  Stat Sin       Date:  2010-04-01       Impact factor: 1.261

5.  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

  5 in total

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