TY - JOUR
T1 - Regression analysis of clustered failure time data with informative cluster size under the additive transformation models
AU - Chen, Ling
AU - Feng, Yanqin
AU - Sun, Jianguo
N1 - Publisher Copyright:
© 2016, Springer Science+Business Media New York.
PY - 2017/10/1
Y1 - 2017/10/1
N2 - 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.
AB - 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.
KW - Additive transformation model
KW - Informative cluster size
KW - Weighted estimating equation
KW - Within-cluster resampling
UR - http://www.scopus.com/inward/record.url?scp=84991746919&partnerID=8YFLogxK
U2 - 10.1007/s10985-016-9384-x
DO - 10.1007/s10985-016-9384-x
M3 - Article
C2 - 27761797
AN - SCOPUS:84991746919
SN - 1380-7870
VL - 23
SP - 651
EP - 670
JO - Lifetime Data Analysis
JF - Lifetime Data Analysis
IS - 4
ER -