The power of focused tests to detect disease clustering
Statistics in Medicine, ISSN: 1097-0258, Vol: 14, Issue: 21-22, Page: 2291-2308
1995
- 38Citations
- 10Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations38
- Citation Indexes37
- 37
- CrossRef31
- Policy Citations1
- Policy Citation1
- Captures10
- Readers10
- 10
Article Description
Statistical tests have been proposed for determining whether incident cases of adverse health effects are ‘clustered’ together. Several procedures, termed ‘focused’, specifically analyse disease surveillance data around pre‐specified putative sources of environmental hazard. Little has been done to compare the performance of various proposed methods on actual models of clustering. Analytic power functions are derived for three tests of focused clustering. These functions are based on the probabilistic structure of the clustering tests and do not require simulation. The three tests are compared with respect to statistical power on hypothetical data where monotone multiplicative increases in disease risk near a putative hazard define disease clusters of varying intensity. Copyright © 1995 John Wiley & Sons, Ltd.
Bibliographic Details
Wiley
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