Primer on statistical interpretation or methods report card on propensity-score matching in the cardiology literature from 2004 to 2006: a systematic review.
BACKGROUND
Propensity-score matching is frequently used in the cardiology literature. Recent systematic reviews have found that this method is, in general, poorly implemented in the medical literature. The study objective was to examine the quality of the implementation of propensity-score matching in the general cardiology literature.


RESULTS
A total of 44 articles published in the American Heart Journal, the American Journal of Cardiology, Circulation, the European Heart Journal, Heart, the International Journal of Cardiology, and the Journal of the American College of Cardiology between January 1, 2004, and December 31, 2006, were examined. Twenty of the 44 studies did not provide adequate information on how the propensity-score-matched pairs were formed. Fourteen studies did not report whether matching on the propensity score balanced baseline characteristics between treated and untreated subjects in the matched sample. Only 4 studies explicitly used statistical methods appropriate for matched studies to compare baseline characteristics between treated and untreated subjects. Only 11 (25%) of the 44 studies explicitly used statistical methods appropriate for the analysis of matched data when estimating the effect of treatment on the outcomes. Only 2 studies described the matching method used, assessed balance in baseline covariates by appropriate methods, and used appropriate statistical methods to estimate the treatment effect and its significance.


CONCLUSIONS
Application of propensity-score matching was poor in the cardiology literature. Suggestions for improving the reporting and analysis of studies that use propensity-score matching are provided.
Austin
Peter C
PC
Institute for Clinical Evaluative Sciences, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario M4N 3M5, Canada. peter.austin@ices.on.ca
D002309:Cardiology / Q000379:methods / Q000706:statistics & numerical data*; D003627:Data Interpretation, Statistical; D005069:Evaluation Studies as Topic; D006287:Health Planning Guidelines; D006801:Humans; D010506:Periodicals as Topic; D057216:Propensity Score; D012107:Research Design