Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity‐score matched samples
Top Cited Papers
Open Access
- 13 October 2009
- journal article
- research article
- Published by Wiley in Statistics in Medicine
- Vol. 28 (25), 3083-3107
- https://doi.org/10.1002/sim.3697
Abstract
The propensity score is a subject's probability of treatment, conditional on observed baseline covariates. Conditional on the true propensity score, treated and untreated subjects have similar distributions of observed baseline covariates. Propensity‐score matching is a popular method of using the propensity score in the medical literature. Using this approach, matched sets of treated and untreated subjects with similar values of the propensity score are formed. Inferences about treatment effect made using propensity‐score matching are valid only if, in the matched sample, treated and untreated subjects have similar distributions of measured baseline covariates. In this paper we discuss the following methods for assessing whether the propensity score model has been correctly specified: comparing means and prevalences of baseline characteristics using standardized differences; ratios comparing the variance of continuous covariates between treated and untreated subjects; comparison of higher order moments and interactions; five‐number summaries; and graphical methods such as quantile–quantile plots, side‐by‐side boxplots, and non‐parametric density plots for comparing the distribution of baseline covariates between treatment groups. We describe methods to determine the sampling distribution of the standardized difference when the true standardized difference is equal to zero, thereby allowing one to determine the range of standardized differences that are plausible with the propensity score model having been correctly specified. We highlight the limitations of some previously used methods for assessing the adequacy of the specification of the propensity‐score model. In particular, methods based on comparing the distribution of the estimated propensity score between treated and untreated subjects are uninformative. Copyright © 2009 John Wiley & Sons, Ltd.Keywords
This publication has 40 references indexed in Scilit:
- Type I Error Rates, Coverage of Confidence Intervals, and Variance Estimation in Propensity-Score Matched AnalysesThe International Journal of Biostatistics, 2009
- Primer on Statistical Interpretation or Methods Report Card on Propensity-Score Matching in the Cardiology Literature From 2004 to 2006Circulation: Cardiovascular Quality and Outcomes, 2008
- A critical appraisal of propensity‐score matching in the medical literature between 1996 and 2003Statistics in Medicine, 2007
- Outcomes in ambulatory chronic systolic and diastolic heart failure: A propensity score analysisAmerican Heart Journal, 2006
- Missed opportunities in the secondary prevention of myocardial infarction: An assessment of the effects of statin underprescribing on mortalityAmerican Heart Journal, 2006
- A review of the application of propensity score methods yielded increasing use, advantages in specific settings, but not substantially different estimates compared with conventional multivariable methodsJournal of Clinical Epidemiology, 2006
- Heart failure, chronic diuretic use, and increase in mortality and hospitalization: an observational study using propensity score methodsEuropean Heart Journal, 2006
- Reducing Bias in Observational Studies Using Subclassification on the Propensity ScoreJournal of the American Statistical Association, 1984
- Designs for Experiments — Parallel Comparisons of TreatmentThe New England Journal of Medicine, 1983
- The central role of the propensity score in observational studies for causal effectsBiometrika, 1983