Prognostic score–based balance measures for propensity score methods in comparative effectiveness research

By Elizabeth A. Stuart, Brian K. Lee, Finbarr P. Leacy. Journal of Clinical Epidemiology, 2013

719 citations recorded by the source corpus.

Objective Examining covariate balance is the prescribed method for determining when propensity score methods are successful at reducing bias. This study assessed the performance of various balance measures, including a proposed balance measure based on the prognostic score (also known as the disease-risk score), to determine which balance measures best correlate with bias in the treatment effect estimate. Study Design and Setting The correlations of multiple common balance measures with bias in the treatment effect estimate produced by weighting by the odds, subclassification on the propensity score, and full matching on the propensity score were calculated. Simulated data were used, based on realistic data settings. Settings included both continuous and binary covariates and continuous covariates only. Results The standardized mean difference in prognostic scores, the mean standardized mean difference, and the mean t-statistic all had high correlations with bias in the effect estimate. Overall, prognostic scores displayed the highest correlations of all the balance measures considered. Prognostic score measure performance was generally not affected by model misspecification and performed well under a variety of scenarios. Conclusion Researchers should consider using prognostic score–based balance measures for assessing the performance of propensity score methods for reducing bias in non-experimental studies.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/1f53e0d599af68bed96a186580f0db3a0eb6cd21

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Citations in the corpus

Balance diagnostics after propensity score matching. (2019, background): “Simulation study has demonstrated that the prognostic score greatly outperforms mean differences on covariates and significance tests in assessing balance (7).”
Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation (2020, background): “Lastly, under these conditions, the hyper-boxes are designed to provide balance on relevant covariates and thus lead to high quality treatment effect estimates (Stuart et al. 2013).”