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Activity Number: 250
Type: Contributed
Date/Time: Monday, August 1, 2016 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #319456 View Presentation
Title: Regression Analysis of Clustered Failure Time Data with Informative Cluster Size Under the Additive Transformation Models
Author(s): Ling Chen* and Yanqin Feng and Jianguo Sun
Companies: Washington University in St. Louis and Wuhan University and University of Missouri
Keywords: additive transformation model ; informative cluster size ; within-cluster resampling ; weighted estimating equation

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.

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