title: A spatial scan statistic for survival data.
abstract: 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 functions including the exponential, gamma, and log-normal distributions. We also present a method to adjust the analysis for covariates. The cluster detection method is illustrated using survival data for men diagnosed with prostate cancer in Connecticut from 1984 to 1995.
authors: Statistical Research and Applications Branch, Division of Cancer Control and Population Sciences, National Cancer Institute (Contractor), 6116 Executive Boulevard, Rockville, Maryland 20852, USA. huangla@mail.nih.gov
authors: Lan
authors: L
authors: Huang
authors: Martin
authors: M
authors: Kulldorff
authors: David
authors: D
authors: Gregorio
mesh_terms: D000704:Analysis of Variance; D003237:Connecticut; D005843:Geography; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D016012:Poisson Distribution; D011237:Predictive Value of Tests; D012680:Sensitivity and Specificity; D016019:Survival Analysis