Confidence intervals for spatial scan statistic.

Abstract

The spatial scan statistic is a popular statistical tool to detect geographical clusters of diseases. The basic problem of constructing confidence intervals for the relative risk of the most likely cluster has remained an open question. To cover this lack, a Monte Carlo based interval estimator for the relative risk of the primary cluster is derived. The method works for the circular spatial scan statistic applied to binomial data, and it ensures, by construction, an analytical control of the coverage probability under the nominal confidence coefficient. In addition, its performance is illustrated on simulated and real data of birth defects in New York State.