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February 25, 2016BMJ803 citationsOpen Access

Analysis of matched case-control studies

NPNeil Pearce

Key Points

  • The aim of this paper is to clarify misconceptions about matching and confounding in case-control studies.
  • Examined common misconceptions regarding confounding in matched case-control studies.
  • Discussed the effects of matching factors on confounding control.
  • Introduced standard (unconditional) and matched (conditional) analyses for data evaluation.
  • Matching does not eliminate confounding by matching factors; it may introduce new confounding.
  • Standard analysis can control for matching factors without loss of validity or precision.
  • Matched analysis is not always necessary or appropriate, depending on the data characteristics.

Abstract

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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Cite This Study

Neil Pearce (2016) studied this question.

synapsesocial.com/papers/69e07500f289ddaa86459087https://doi.org/10.1136/bmj.i969
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