Cargando…

Testing for clustering at many ranges inflates family-wise error rate (FWE)

BACKGROUND: Testing for clustering at multiple ranges within a single dataset is a common practice in spatial epidemiology. It is not documented whether this approach has an impact on the type 1 error rate. METHODS: We estimated the family-wise error rate (FWE) for the difference in Ripley’s K funct...

Descripción completa

Detalles Bibliográficos
Autores principales: Loop, Matthew Shane, McClure, Leslie A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4326498/
https://www.ncbi.nlm.nih.gov/pubmed/25588612
http://dx.doi.org/10.1186/1476-072X-14-4
_version_ 1782356943502835712
author Loop, Matthew Shane
McClure, Leslie A
author_facet Loop, Matthew Shane
McClure, Leslie A
author_sort Loop, Matthew Shane
collection PubMed
description BACKGROUND: Testing for clustering at multiple ranges within a single dataset is a common practice in spatial epidemiology. It is not documented whether this approach has an impact on the type 1 error rate. METHODS: We estimated the family-wise error rate (FWE) for the difference in Ripley’s K functions test, when testing at an increasing number of ranges at an alpha-level of 0.05. Case and control locations were generated from a Cox process on a square area the size of the continental US (≈3,000,000 mi(2)). Two thousand Monte Carlo replicates were used to estimate the FWE with 95% confidence intervals when testing for clustering at one range, as well as 10, 50, and 100 equidistant ranges. RESULTS: The estimated FWE and 95% confidence intervals when testing 10, 50, and 100 ranges were 0.22 (0.20 - 0.24), 0.34 (0.31 - 0.36), and 0.36 (0.34 - 0.38), respectively. CONCLUSIONS: Testing for clustering at multiple ranges within a single dataset inflated the FWE above the nominal level of 0.05. Investigators should construct simultaneous critical envelopes (available in spatstat package in R), or use a test statistic that integrates the test statistics from each range, as suggested by the creators of the difference in Ripley’s K functions test.
format Online
Article
Text
id pubmed-4326498
institution National Center for Biotechnology Information
language English
publishDate 2015
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-43264982015-02-14 Testing for clustering at many ranges inflates family-wise error rate (FWE) Loop, Matthew Shane McClure, Leslie A Int J Health Geogr Research BACKGROUND: Testing for clustering at multiple ranges within a single dataset is a common practice in spatial epidemiology. It is not documented whether this approach has an impact on the type 1 error rate. METHODS: We estimated the family-wise error rate (FWE) for the difference in Ripley’s K functions test, when testing at an increasing number of ranges at an alpha-level of 0.05. Case and control locations were generated from a Cox process on a square area the size of the continental US (≈3,000,000 mi(2)). Two thousand Monte Carlo replicates were used to estimate the FWE with 95% confidence intervals when testing for clustering at one range, as well as 10, 50, and 100 equidistant ranges. RESULTS: The estimated FWE and 95% confidence intervals when testing 10, 50, and 100 ranges were 0.22 (0.20 - 0.24), 0.34 (0.31 - 0.36), and 0.36 (0.34 - 0.38), respectively. CONCLUSIONS: Testing for clustering at multiple ranges within a single dataset inflated the FWE above the nominal level of 0.05. Investigators should construct simultaneous critical envelopes (available in spatstat package in R), or use a test statistic that integrates the test statistics from each range, as suggested by the creators of the difference in Ripley’s K functions test. BioMed Central 2015-01-15 /pmc/articles/PMC4326498/ /pubmed/25588612 http://dx.doi.org/10.1186/1476-072X-14-4 Text en © Loop and McClure; licensee BioMed Central. 2015 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Loop, Matthew Shane
McClure, Leslie A
Testing for clustering at many ranges inflates family-wise error rate (FWE)
title Testing for clustering at many ranges inflates family-wise error rate (FWE)
title_full Testing for clustering at many ranges inflates family-wise error rate (FWE)
title_fullStr Testing for clustering at many ranges inflates family-wise error rate (FWE)
title_full_unstemmed Testing for clustering at many ranges inflates family-wise error rate (FWE)
title_short Testing for clustering at many ranges inflates family-wise error rate (FWE)
title_sort testing for clustering at many ranges inflates family-wise error rate (fwe)
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4326498/
https://www.ncbi.nlm.nih.gov/pubmed/25588612
http://dx.doi.org/10.1186/1476-072X-14-4
work_keys_str_mv AT loopmatthewshane testingforclusteringatmanyrangesinflatesfamilywiseerrorratefwe
AT mcclurelesliea testingforclusteringatmanyrangesinflatesfamilywiseerrorratefwe