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Journal article
Prognostic score-based balance measures can be a useful diagnostic for propensity score methods in comparative effectiveness research
Journal of clinical epidemiology, v 66(8), pp S84-S90
01 Aug 2013
PMID: 23849158
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Objective: Examining covariate balance is the prescribed method for determining the degree to which propensity score methods should be successful at reducing bias. This study assessed the performance of various balance measures, including a proposed balance measure based on the prognostic score (similar to a 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 absolute standardized mean difference (ASMD) in prognostic scores, the mean ASMD (in covariates), and the mean t-statistic all had high correlations with bias in the effect estimate. Overall, prognostic scores displayed the highest correlations with bias of all the balance measures considered. Prognostic score measure performance was generally not affected by model misspecification, and the prognostic score measure 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 nonexperimental studies. (C) 2013 Elsevier Inc. All rights reserved.
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Details
- Title
- Prognostic score-based balance measures can be a useful diagnostic for propensity score methods in comparative effectiveness research
- Creators
- Elizabeth A. Stuart - Johns Hopkins UniversityBrian K. Lee - Drexel UniversityFinbarr P. Leacy - MRC Biostatistics Unit
- Publication Details
- Journal of clinical epidemiology, v 66(8), pp S84-S90
- Publisher
- Elsevier
- Number of pages
- 7
- Grant note
- K25MH083846 / National Institute of Mental Health; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA; NIH National Institute of Mental Health (NIMH) MC_U105260558 / MRC; UK Research & Innovation (UKRI); Medical Research Council UK (MRC) MC_U105260558 / Medical Research Council; UK Research & Innovation (UKRI); Medical Research Council UK (MRC); European Commission
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Epidemiology and Biostatistics
- Web of Science ID
- WOS:000322207400012
- Scopus ID
- 2-s2.0-84880240208
- Other Identifier
- 991019169618204721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Health Care Sciences & Services
- Public, Environmental & Occupational Health