A Multiresolution Hazard Model for Multicenter Survival Studies: Application to Tamoxifen Treatment in Early Stage...

In multicenter studies, one often needs to make inference about a population survival curve based on multiple, possibly heterogeneous survival data from individual centers. We investigate a flexible Bayesian method for estimating a population survival curve based on a semiparametric multiresolution hazard model that can incorporate covariates and account for center heterogeneity. The method yields a smooth estimate of the survival curve for "multiple resolutions" or time scales of interest. The Bayesian model used has the capability to accommodate general forms of censoring and a priori smoothness assumptions. We develop a model checking and diagnostic technique based on the posterior predictive distribution and use it to identify departures from the model assumptions. The hazard estimator is used to analyze data from 110 centers that participated in a multicenter randomized clinical trial to evaluate tamoxifen in the treatment of early stage breast cancer. Of particular interest are the estimates of center heterogeneity in the baseline hazard curves and in the treatment effects, after adjustment for a few key clinical covariates. Our analysis suggests that the treatment effect estimates are rather robust, even for a collection of small trial centers, despite variations in center characteristics.