A spatial scan statistic for survival data

Spatial scan statistics with Bernoulli and Poisson models are commonly used for geographical disease surveillance and cluster detection. These models. suitable for count data were not designed for data with continuous outcomes. We propose a spatial scan statistic based on an exponential model to handle either uncensored or censored continuous survival data. The power and sensitivity of the developed model are investigated through intensive simulations. The method performs well for different survival distribution finictious including tile exponential, gainnia, and log-norinal distributions. We also present a method to adjust the analysis for covariates. The cluster detection rnethod is illustrated rising survival data for inen diagnosed with prostate cancer in Connecticut from 1984 to 1995.