This article introduces a new approach for evaluating the quality of healthcare providers, including an integrated solution to several problems that limit the usefulness of available methods. Our approach combines information from all the quality indicators available for a provider (e.g., from other years, other patients, or other indicators for the same patients) to estimate more accurately the providers previous or expected quality. The approach also provides an empirical basis for comparing and combining alternative quality indicators, thereby enabling policy makers to choose among potential indicators, explore the potential bias in alternative measures, and increase the value of quality measures for assessing and improving care. Using hospital care for elderly heart attack patients as an example, we demonstrate that these methods can be used to create reliable, precise predictions about provider quality. Comparing quality of care across providers may be far more feasible than many now believe.
Proportional hazard Cox regression models are frequently used to analyze the impact of different factors on time-to-event outcomes. Most practitioners are familiar with and interpret research results in terms of hazard ratios. Direct differences in survival curves are, however, easier to understand for the general population of users and to visualize graphically. Analyzing the difference among the survival curves for the population at risk allows easy interpretation of the impact of a therapy over the follow-up. When the available information is obtained from observational studies, the observed results are potentially subject to a plethora of measured and unmeasured confounders. Although there are procedures to adjust survival curves for measured covariates, the case of unmeasured confounders has not yet been considered in the literature. In this article we provide a semi-parametric procedure for adjusting survival curves for measured and unmeasured confounders. The method augments our novel instrumental variable estimation method for survival time data in the presence of unmeasured confounding with a procedure for mapping estimates onto the survival probability and the expected survival time scales.