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第41行： − − When the outcome of interest is binary, the most general tool for the analysis of matched data is [[conditional logistic regression]] as it handles strata of arbitrary size and continuous or binary treatments (predictors) and can control for covariates. In particular cases, simpler tests like [[paired difference test]], [[McNemar test]] and [[Cochran-Mantel-Haenszel test]] are available.
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== Analysis ==
== Analysis ==
When the outcome of interest is binary, the most general tool for the analysis of matched data is [[conditional logistic regression]] as it handles strata of arbitrary size and continuous or binary treatments (predictors) and can control for covariates. In particular cases, simpler tests like [[paired difference test]], [[McNemar test]] and [[Cochran-Mantel-Haenszel test]] are available.
When the outcome of interest is binary, the most general tool for the analysis of matched data is conditional logistic regression as it handles strata of arbitrary size and continuous or binary treatments (predictors) and can control for covariates. In particular cases, simpler tests like paired difference test, McNemar test and Cochran-Mantel-Haenszel test are available.
When the outcome of interest is binary, the most general tool for the analysis of matched data is conditional logistic regression as it handles strata of arbitrary size and continuous or binary treatments (predictors) and can control for covariates. In particular cases, simpler tests like paired difference test, McNemar test and Cochran-Mantel-Haenszel test are available.
Matching can also be used to "pre-process" a sample before analysis via another technique, such as regression analysis.
Matching can also be used to "pre-process" a sample before analysis via another technique, such as regression analysis.