ESTIMATING THE MEAN HAZARD RATIO PARAMETERS FOR CLUSTERED SURVIVAL-DATA WITH RANDOM CLUSTERS

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. W e use a maximum pseudo-likelihood estimator to estimate the mean hazar d ratio parameters. We propose a bootstrap sampling scheme to obtain a n estimate of the variance of the proposed estimator. Application of t his 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 da ta example from the Studies of Left Ventricular Dysfunction (SOLVD) Pr evention Trial illustrates the method. (C) 1997 by John Wiley & Sons, Ltd.