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Matched or unmatched analyses with propensity‐score–matched data?
Author(s) -
Wan Fei
Publication year - 2018
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7976
Subject(s) - propensity score matching , statistics , confounding , linear regression , observational study , mathematics , intraclass correlation , matching (statistics) , statistical power , regression analysis , linear model , generalized linear model , regression , type i and type ii errors , econometrics , psychometrics
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.