title: Regression analysis of clustered failure time data with informative cluster size under the additive transformation models.
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.
authors.affiliation: Division of Biostatistics, Washington University School of Medicine, Campus Box 8067, 660 S. Euclid Ave, St. Louis, MO, 63110, USA. lingchen@wustl.edu.
authors.forename: Ling
authors.initials: L
authors.lastname: Chen
authors.affiliation: School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China.|Computational Science Hubei Key Laboratory, Wuhan University, Wuhan, 430072, China.
authors.forename: Yanqin
authors.initials: Y
authors.lastname: Feng
authors.affiliation: Department of Statistics, University of Missouri, 146 Middlebush Hall, Columbia, MO, 65211, USA.
authors.forename: Jianguo
authors.initials: J
authors.lastname: Sun
mesh_terms: D055113:Chronic Periodontitis / Q000150:complications / Q000628:therapy; D016000:Cluster Analysis; D003198:Computer Simulation; D006801:Humans; D053208:Kaplan-Meier Estimate; D015993:Life Tables; D015233:Models, Statistical; D012044:Regression Analysis; D012307:Risk Factors; D016388:Tooth Loss / Q000209:etiology
keywords: Additive transformation model; Informative cluster size; Weighted estimating equation; Within-cluster resampling