PubMed HealthSearch

PubMed · 8524715

Matching.

Abstract

Matching is an intuitively appealing design strategy for ensuring balance on one or more potential confounding variables, usually either among subjects who were exposed or unexposed to a suspected risk factor for disease in a cohort study or between diseased and nondiseased subjects in a case-control study. But does matching always automatically "control" confounding and is it always as good a strategy as it seems? It is the intention of this review to shed light on these questions primarily through illustrative examples of the effects of matching on the validity of point estimates of the odds ratio between exposure and disease status in both types of study designs. It is seen that the results of matching are more or less in line with expectations in cohort studies, but that matching can lead to unexpected results in case-control studies. In a case-control study, confounding is not automatically controlled by matching per se; rather, matching and a statistical analysis that properly accounts for the matching are needed to obtain a valid estimate of effect in a case-control study design.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

M C Costanza. 1995. Matching.. https://doi.org/10.1006/pmed.1995.1069

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Tobacco research.

Explore the source record for details and available documents.

Confounding Factors, Epidemiologic

Mismeasurement and the resonance of strong confounders: uncorrelated errors.

Greenland first documented (Am J Epidemiol 1980; 112:564-9) that error in the measurement of a confounder could resonate--that it could bias estimates of other study variables, and that the bias could persist even with statistical adjustment for the confounder as measured. An important question is raised by this finding: can such bias be more than trivial within the bounds of realistic data configurations? The authors examine several situations involving dichotomous and continuous data in which a confounder and a null variable are measured with error, and they assess the extent of resultant bias in estimates of the effect of the null variable. They show that, with continuous variables, measurement error amounting to 40% of observed variance in the confounder could cause the observed impact of the null study variable to appear to alter risk by as much as 30%. Similarly, they show, with dichotomous independent variables, that 15% measurement error in the form of misclassification could lead the null study variable to appear to alter risk by as much as 50%. Such bias would result only from strong confounding. Measurement error would obscure the evidence that strong confounding is a likely problem. These results support the need for every epidemiologic inquiry to include evaluations of measurement error in each variable considered.

Confounding Factors, Epidemiologic