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Analysis of matched case-control studies

There are two common misconceptions about case-control studies: that matching in itself eliminates (controls) confounding by the matching factors, and that if matching has been performed, then a “matched analysis” is required. However, matching in a case-control study does not control for confoundin...

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Autor principal: Pearce, Neil
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group Ltd. 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4770817/
https://www.ncbi.nlm.nih.gov/pubmed/26916049
http://dx.doi.org/10.1136/bmj.i969
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author Pearce, Neil
author_facet Pearce, Neil
author_sort Pearce, Neil
collection PubMed
description There are two common misconceptions about case-control studies: that matching in itself eliminates (controls) confounding by the matching factors, and that if matching has been performed, then a “matched analysis” is required. However, matching in a case-control study does not control for confounding by the matching factors; in fact it can introduce confounding by the matching factors even when it did not exist in the source population. Thus, a matched design may require controlling for the matching factors in the analysis. However, it is not the case that a matched design requires a matched analysis. Provided that there are no problems of sparse data, control for the matching factors can be obtained, with no loss of validity and a possible increase in precision, using a “standard” (unconditional) analysis, and a “matched” (conditional) analysis may not be required or appropriate.
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spelling pubmed-47708172016-03-10 Analysis of matched case-control studies Pearce, Neil BMJ Research Methods & Reporting There are two common misconceptions about case-control studies: that matching in itself eliminates (controls) confounding by the matching factors, and that if matching has been performed, then a “matched analysis” is required. However, matching in a case-control study does not control for confounding by the matching factors; in fact it can introduce confounding by the matching factors even when it did not exist in the source population. Thus, a matched design may require controlling for the matching factors in the analysis. However, it is not the case that a matched design requires a matched analysis. Provided that there are no problems of sparse data, control for the matching factors can be obtained, with no loss of validity and a possible increase in precision, using a “standard” (unconditional) analysis, and a “matched” (conditional) analysis may not be required or appropriate. BMJ Publishing Group Ltd. 2016-02-25 /pmc/articles/PMC4770817/ /pubmed/26916049 http://dx.doi.org/10.1136/bmj.i969 Text en Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 3.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/3.0/.
spellingShingle Research Methods & Reporting
Pearce, Neil
Analysis of matched case-control studies
title Analysis of matched case-control studies
title_full Analysis of matched case-control studies
title_fullStr Analysis of matched case-control studies
title_full_unstemmed Analysis of matched case-control studies
title_short Analysis of matched case-control studies
title_sort analysis of matched case-control studies
topic Research Methods & Reporting
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4770817/
https://www.ncbi.nlm.nih.gov/pubmed/26916049
http://dx.doi.org/10.1136/bmj.i969
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