Detecting Disease Clusters

funded by National Institutes of Health (NIH)
and by New York State Science and Technology Foundation

        One the difficulties faced by health professionals in detecting of disease clusters is that the data sets are often small and inferences must be based on a relative handful of observations. It is crucial for the health professional to know what statistical tests are best in these small-environment problems, and to have these methods available in a user-friendly computer package. A variety of new, rapid, and exact combinatorial expressions for cluster analysis of patterns of disease have been proposed. Investigations into the statistical power of both these and other previously published methods for cluster detection in structured small environments will be used to recommend different tests for different kinds of problems and different amounts of data.

        An interactive program called EPIC (Exact Probabilities for Incidence Clustering) that includes an intuitive interface and a thorough set of tutorials and guidelines will help the professional choose the best statistical test for a particular problem. EPIC will allow the health professional to investigate allegations of disease clustering within small, structured environments, such as families, sibships, wards, classrooms, cell blocks, job types, age classes or locations within a building. When sample sizes are small (as is almost always the case in real circumstances of public health concern), exact statistical methods are necessary since the approximate methods usually used only yield accurate estimates with data sets are large.

        Exact methods guarantee that Type I error can be controlled to any desired level. Although a few exact methods based on matrix occupancy models have previously been described for data sets with perfectly regular structure, no exact methods were applicable when, for instance, families were of different sizes. EPIC will provide, for the first time, general exact statistical methods for use with small data sets in structured environments. It will allow public health professionals in the research and regulatory communities access to these new methods in a flexible and powerful microcomputer implementation.

Ferson, S. 1996. Reliable calculation in probabilistic logic: accounting for small sample size and model uncertainty. Proceedings of Intelligent Systems: A Semiotic Perspective. National Institute for Standards and Technology, Gaithersburg, Maryland [to appear].

Also see: List of AB Publications

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