Small area variations in surgical rates: Simulation as an aid in interpretation of findings.

Abstract

Purpose. To explore Monte Carlo simulation for interpretation of small area variations in surgical rates. Methods. Simulation was used to generate sets of surgical rates under the null hypothesis of equal rates. The distributions of the extremal quotient, coefficient of variation, chi square, systematic component of variation, and case count were described. The null hypothesis was modified to allow reasonable variability. Results. The chi-square, CV, and CC had interpretable values and adequate power over the range of parameters tested. The EQ and the SCV did not. Only two of the five operations studied had greater variability than expected under a modified null hypothesis, compared with five out of five under traditional testing. Conclusions. Simulation can estimate the distributions of nonstandard statistics. The new defined case count adds valuable policy-relevant information. Modification of the null hypothesis improves decisions about which variations need further investigation.