We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Scale and shape issues in focused cluster power for count data.
- Authors
Puett, Robin C.; Lawson, Andrew B.; Clark, Allan B.; Aldrich, Tim E.; Porter, Dwayne E.; Feigley, Charles E.; Hebert, James R.
- Abstract
Background: Interest in the development of statistical methods for disease cluster detection has experienced rapid growth in recent years. Evaluations of statistical power provide important information for the selection of an appropriate statistical method in environmentally-related disease cluster investigations. Published power evaluations have not yet addressed the use of models for focused cluster detection and have not fully investigated the issues of disease cluster scale and shape. As meteorological and other factors can impact the dispersion of environmental toxicants, it follows that environmental exposures and associated diseases can be dispersed in a variety of spatial patterns. This study simulates disease clusters in a variety of shapes and scales around a centrally located single pollution source. We evaluate the power of a range of focused cluster tests and generalized linear models to detect these various cluster shapes and scales for count data. Results: In general, the power of hypothesis tests and models to detect focused clusters improved when the test or model included parameters specific to the shape of cluster being examined (i.e. inclusion of a function for direction improved power of models to detect clustering with an angular effect). However, power to detect clusters where the risk peaked and then declined was limited. Conclusion: Findings from this investigation show sizeable changes in power according to the scale and shape of the cluster and the test or model applied. These findings demonstrate the importance of selecting a test or model with functions appropriate to detect the spatial pattern of the disease cluster.
- Subjects
DISEASES; SPATIAL analysis (Statistics); ENVIRONMENTAL toxicology; TOXINS; POLLUTION; LINEAR statistical models; MATHEMATICAL models
- Publication
International Journal of Health Geographics, 2005, Vol 4, p8
- ISSN
1476-072X
- Publication type
Article
- DOI
10.1186/1476-072X-4-8