title: When does it pay to break the matches for analysis of a matched-pairs design?
abstract: Two methods of analysis are compared to estimate the treatment effect of a comparative study where each treated individual is matched with a single control at the design stage. The usual matched-pairs analysis accounts for the pairing directly in its model, whereas regression adjustment ignores the matching but instead models the pairing using a set of covariates. For a normal linear model, the estimated treatment effect from the matched-pairs analysis (paired t-test) is more efficient. For a Bernoulli logistic model, matched-pairs analysis performs better when the sample size is small, but is inferior to logistic regression for large sample sizes.
authors.affiliation: Biometrics Unit, Cornell University, Ithaca, New York 14853.
authors.forename: H S
authors.initials: HS
authors.lastname: Lynn
authors.forename: C E
authors.initials: CE
authors.lastname: McCulloch
mesh_terms: D002986:Clinical Trials as Topic / Q000379:methods*; D006801:Humans; D008433:Mathematics; D015233:Models, Statistical; D012044:Regression Analysis; D012984:Software