In statistics, matching is comparing groups in which each subject is matched by a comparable subject (called matched or paired control) in terms of age and all other measurable parameters,[1] optimally being equal in every aspect except the exposure of interest. It can be used in case-control studies.
It is possible to reduce, but not necessarily eliminate, the effects of confounding variables by matching. Matched pairs are constructed based on additional variables that are thought to serve as confounders. By pairing students whose values on the confounding variables are similar, a greater fraction of the difference in the value of interest is due to the factor of interest, and a lesser fraction is due to the confounder.
Analysis
Paired difference tests can be applied to analyze such matched studies. Forming matched pairs for paired difference testing is an example of a general approach for using matching to reduce the effects of confounding when making comparisons.[2][3][4] For example, the Rubin Causal Model uses matching to estimate causal effects.
Overmatching
Overmatching is matching for an apparent confounder that actually is a result of the exposure. True confounders are associated with both the exposure and the disease, but if the exposure itself leads to the confounder, or has equal status with it, then stratifying by that confounder will also partly stratify by the exposure, resulting in an obscured relation of the exposure to the disease.[5] Overmatching thus causes statistical bias.[5]
For example, matching the control group by gestation length and/or the number of multiple births when estimating perinatal mortality and birthweight after in vitro fertilization (IVF) is overmatching, since IVF itself increases the risk of premature birth and multiple birth.[6]
It may be regarded as a sampling bias in decreasing the external validity of a study, because the controls become more similar to the cases in regard to exposure than the general population.
References
- ^ TheFreeDictionary --> matched study Retrieved on October 9, 2009
- ^ Rubin, Donald B. (1973). "Matching to Remove Bias in Observational Studies". Biometrics 29 (1): 159–183. http://www.jstor.org/stable/2529684.
- ^ Anderson, Dallas W.; Kish, Leslie; Cornell, Richard G. (1980). "On Stratification, Grouping and Matching". Scandinavian Journal of Statistics 7 (2): 61–66. http://www.jstor.org/stable/4615774.
- ^ Kupper, Lawrence L.; Karon, John M.; Kleinbaum, David G.; Morgenstern, Hal; Lewis, Donald K. (1981). "Matching in Epidemiologic Studies: Validity and Efficiency Considerations". Biometrics 37 (2): 271–291. http://www.jstor.org/stable/2530417.
- ^ a b Removal of radiation dose response effects: an example of over-matching. Marsh JL, Hutton JL, Binks K. PMID: 12169512
- ^ The danger of overmatching in studies of the perinatal mortality and birthweight of infants born after assisted conception. Gissler M, Hemminki E. PMID: 8902436
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