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

Ling Chen (), Yanqin Feng and Jianguo Sun
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Ling Chen: Washington University School of Medicine
Yanqin Feng: Wuhan University
Jianguo Sun: University of Missouri

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2017, vol. 23, issue 4, No 7, 670 pages

Abstract: 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; Within-cluster resampling; Weighted estimating equation (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s10985-016-9384-x

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