In this study, an abbreviated introduction to hierarchical statistical models for quantifying and explaining variations in the utilization of medical care is presented. The illustrative example was derived from an analysis of interstate variation in coronary angiography utilization for Medicare patients with a recent acute myocardial infarction. The hierarchical model distinguished within-from between-states variation: the former was modeled via a separate logistic regression for each state, with age and sex as the independent variables, while the latter was modeled via a multivariate normal distribution for the coefficients of the state-specific logistic models. Alternative computation approaches were compared and model fit was assessed. Estimates of the distribution of state rates of angiography for an average patient and for age-by-sex strata were obtained. The results showed substantial interstate variation in angiography utilization, but only moderate interstate variation in the effects of age and sex on the decision to perform angiography. This analytic approach allows substantially more detailed results than those by standardization, and accounts for sample size differences between units of aggregation. The next major step in the analysis would be to derive smoothed estimates of the individual state logistic models by pooling data across states. The analysis can also be extended to incorporate other patient characteristics, such as race and comorbidity, and state characteristics, such as geographic location and availability of the procedure.