title: Estimating the mean hazard ratio parameters for clustered survival data with random clusters.
abstract: 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.
authors.affiliation: Department of Biostatistics, University of North Carolina at Chapel Hill 27599-7400, USA.
authors.forename: J
authors.initials: J
authors.lastname: Cai
authors.forename: H
authors.initials: H
authors.lastname: Zhou
authors.forename: C E
authors.initials: CE
authors.lastname: Davis
mesh_terms: D000328:Adult; D000806:Angiotensin-Converting Enzyme Inhibitors / Q000627:therapeutic use; D016000:Cluster Analysis; D003198:Computer Simulation; D004311:Double-Blind Method; D004656:Enalapril / Q000627:therapeutic use; D006801:Humans; D016013:Likelihood Functions; D008875:Middle Aged; D016016:Proportional Hazards Models; D016032:Randomized Controlled Trials as Topic; D016019:Survival Analysis; D018487:Ventricular Dysfunction, Left / Q000188:drug therapy
chemical_list: D000806:Angiotensin-Converting Enzyme Inhibitors; D004656:Enalapril