Evaluating inter-hospital variability in mortality rates over time, allowing for time-varying effects

In outcome studies, quality of care in various institutions is typically assessed by comparing observed to expected outcome rates, after adjusting for patients' case-mix factors in logistic regression models. However, differences in patterns of outcome rates over time, especially when there is a distinction between the determinants affecting early and later events, are rarely studied. We use six-month mortality after coronary artery bypass graft operation (CABG) as an example. We present a statistically valid approach to estimate expected survival curves for different subgroups, based on a Cox survival model with time-varying effects. Bootstrap confidence intervals around the expected survival curves are constructed. This approach is applied for examining the pattern of deviation of high-mortality hospitals after CABG. Implications for quality assessment in comparative outcome studies are discussed.