Flexible scan statistic test to detect disease clusters in hierarchical trees

Abstract

This paper presents a flexible scan test statistic to detect disease clusters in data sets represented as a hierarchical tree. The algorithm searches through the branches of the tree and it is able to aggregate leaves located in different branches. The test statistic combines two terms, the log-likelihood of the data and the amount of information necessary to computationally code each potential cluster. This second term penalizes the search algorithm avoiding the detection of oddly shaped clusters and it is based on the Minimum Description Length (MDL) principle. Our MDL method reaches an automatic compromise between bias and variance. We present simulated results showing that its power performance as compared to the usual scan statistic and the high accuracy of the MDL to identify clusters that are scattered on the tree. The MDL method is illustrated with a large database looking at the relationship between occupation and death from silicosis.