A Spatial Scan Statistic for Survival Data

Authors

  • Lan Huang,

    Corresponding author
    1. Statistical Research and Applications Branch, Division of Cancer Control and Population Sciences, National Cancer Institute (Contractor), 6116 Executive Boulevard, Rockville, Maryland 20852, U.S.A.
    Search for more papers by this author
  • Martin Kulldorff,

    1. Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care, Boston, Massachusetts 02215, U.S.A.
    Search for more papers by this author
  • David Gregorio

    1. Department of Community Medicine, University of Connecticut School of Medicine, Farmington, Connecticut 06030, U.S.A.
    Search for more papers by this author

email:huangla@mail.nih.gov

Abstract

Summary 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.

Ancillary