Key result
Matching on continuous variables yields more precise odds ratio estimates than statistical adjustment in case-control studies.
Case-Control
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The paper presents a case-control study involving a disease, exposures and several continuous confounders. The relative efficiency and validity of a fully matched design is compared with random sampling of controls. We test a viable option of a partially matched design when inability to match all study subjects on all confounders occurs. The degree of bias in the odds ratios introduced by the different designs and by the different analytic models is assessed in comparison with the estimates obtained from a total cohort, from which both cases and controls were selected. Matched designs and analytic strategies are also evaluated in terms of the variances of the odds ratios. The results indicate that matching on continuous variables may lead to a more precise estimate of odds ratio than statistical control of confounding in unmatched designs. Partial selection of controls by matching may be a useful strategy when complete matching cannot be achieved; in practice, partial matching achieves most of the benefits of full matching.
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Friedlander et al. (1993) conducted a case-control in Myocardial infarction. Matching on continuous variables (fully or partially matched designs) vs. Random sampling of controls (unmatched design with statistical control) was evaluated on Precision (variance) and bias of odds ratio estimates. Matching on continuous variables in case-control studies led to more precise odds ratio estimates than statistical control of confounding in unmatched designs.
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