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Estimating the mean hazard ratio parameters for clustered survival data with random clusters
Author(s) -
Cai Jianwen,
Zhou Haibo,
Davis C. E.
Publication year - 1997
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(19970915)16:17<2009::aid-sim606>3.0.co;2-r
Subject(s) - estimator , covariate , statistics , hazard ratio , random effects model , hazard , population , cluster sampling , sample size determination , population mean , variance (accounting) , cluster (spacecraft) , simple random sample , mathematics , computer science , econometrics , confidence interval , medicine , meta analysis , chemistry , environmental health , organic chemistry , accounting , business , programming language
We consider a latent variable hazard model for clustered survival data where clusters are a random sample from an underlying population. We allow interactions between the random cluster effect and covariates. We use a maximum pseudo‐likelihood estimator to estimate the mean hazard ratio parameters. We propose a bootstrap sampling scheme to obtain an estimate of the variance of the proposed estimator. Application of this method in large multi‐centre clinical trials allows one to assess the mean treatment effect, where we consider participating centres as a random sample from an underlying population. We evaluate properties of the proposed estimators via extensive simulation studies. A real data example from the Studies of Left Ventricular Dysfunction (SOLVD) Prevention Trial illustrates the method. © 1997 by John Wiley & Sons, Ltd.