Combining matched and unmatched control groups in case-control studies

Saskia Le Cessie, Nico Nagelkerke, Frits R. Rosendaal, Karlijn J. Van Stralen, Elisabeth R. Pomp, Hans C. Van Houwelingen

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

Multiple control groups in case-control studies are used to control for different sources of confounding. For example, cases can be contrasted with matched controls to adjust for multiple genetic or unknown lifestyle factors and simultaneously contrasted with an unmatched population-based control group. Inclusion of different control groups for a single exposure analysis yields several estimates of the odds ratio, all using only part of the data. Here the authors introduce an easy way to combine odds ratios from several case-control analyses with the same cases. The approach is based upon methods used for meta-analysis but takes into account the fact that the same cases are used and that the estimated odds ratios are therefore correlated. Two ways of estimating this correlation are discussed: sandwich methodology and the bootstrap. Confidence intervals for the pooled estimates and a test for checking whether the odds ratios in the separate case-control studies differ significantly are derived. The performance of the method is studied by simulation and by applying the methods to a large study on risk factors for thrombosis, the MEGA Study (1999-2004), wherein cases with first venous thrombosis were included with a matched control group of partners and an unmatched population-based control group.

Original languageEnglish
Pages (from-to)1204-1210
Number of pages7
JournalAmerican Journal of Epidemiology
Volume168
Issue number10
DOIs
Publication statusPublished - Nov 2008
Externally publishedYes

Keywords

  • Bootstrap
  • Case-control studies
  • Control groups
  • Matching
  • Sandwich estimator
  • Venous thrombosis

ASJC Scopus subject areas

  • Epidemiology

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