Propensity-score matching has been used widely in observational studies to balance confounders across treatment groups. However, whether matched-pairs analyses should be used as a primary approach is still in debate. We compared the statistical power and type 1 error rate for four commonly used methods of analyzing propensity-score–matched samples with continuous outcomes: (1) an unadjusted mixed-effects model, (2) an unadjusted generalized estimating method, (3) simple linear regression, and (4) multiple linear regression. Multiple linear regression had the highest statistical power among the four competing methods. We also found that the degree of intraclass correlation within matched pairs depends on the dissimilarity between the coefficient vectors of confounders in the outcome and treatment models. Multiple linear regression is superior to the unadjusted matched-pairs analyses for propensity-score–matched data.
- generalized estimating equation
- intraclass correlation
- linear regression
- mixed effects model
- propensity score matching