A Bayesian model for cluster detection

作者：Wakefield Jonathan^*; Kim Albert

来源：Biostatistics, 2013, 14(4): 752-765.

DOI：10.1093/biostatistics/kxt001

• Bayes factors
• Markov chain Monte Carlo
• Scan statistic
• Spatial epidemiology

The detection of areas in which the risk of a particular disease is significantly elevated, leading to an excess of cases, is an important enterprise in spatial epidemiology. Various frequentist approaches have been suggested for the detection of "clusters" within a hypothesis testing framework. Unfortunately, these suffer from a number of drawbacks including the difficulty in specifying a p-value threshold at which to call significance, the inherent multiplicity problem, and the possibility of multiple clusters. In this paper, we suggest a Bayesian approach to detecting "areas of clustering" in which the study region is partitioned into, possibly multiple, "zones" within which the risk is either at a null, or non-null, level. Computation is carried out using Markov chain Monte Carlo, tuned to the model that we develop. The method is applied to leukemia data in upstate New York.

• 出版日期2013-9