Cluster Analysis

Cluster analysis in space and time

funded by National Institutes of Health (NIH) and the Electric Power Research Institute (EPRI)

Project description

        Diseases such as cancer are often clumped together in terms of their incidence in time or their distribution across space in ways that suggest a common environmental cause or a particular etiology or contagion process. However, humans are likely to perceive clusters even in purely randomly distributed data. The statistical problem is to determine whether there exists an excess of disease incidence--a cluster--above what might be expected by chance alone.

        Some diseases may be clustered in time, so that most cases occur at a particular time of year. Other diseases, like those caused by a point release of toxic chemicals, may be clustered in space, so that most cases occur in the same place. There may also be space-time interaction, like an epidemic wave, so that pairs of cases are close both in time and space.

        Statistical analysis can detect whether cases are clustered in space, in time, or whether there is space-time interaction. Useful statistical analyses include location/date methods such as Mantel's test, Knox test, Cuzick and Edwards case-control spatial clustering test, nearest neighbor statistics, and others. Complementary analyses include area/time-interval methods such as Dat's 0-1 matrix test, the Scan Test, Moran's I and Moran's I adjusted for population size, the empty cells test for rare events, Grimson's proximity test for binary events, Larsen's unimodal clustering test and the Ederer-Myers-Mantel test. Software is needed that brings all of these tests to the health professional in a convenient environment that also supports displaying of disease incidence information in multiple dimensions.

The CAST software was developed under this project. The following publications describe the work in more detail.

Jacquez, G.M. and L.I. Kheifets. 1993. Synthetic cancer variables and the construction and testing of synthetic risk maps. Statistics in Medicine 12: 1931-1942.

Applied Biomathematics. 1993. CAST Version 2.0: Disease Cluster Analysis User Manual. Electric Power Research Institute, Palo Alto, CA.

Oden, N. 1995. Adjusting Moran's I for population density. Statistics in Medicine 15: 783-806.

Oden, N. and G. Jacquez. 1996. Realistic power simulations compare point- and area-based disease cluster tests. Statistics in Medicine 15: 783-806.