Reducing bias using propensity score matching
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Nearly all analyses show Rubin's B<25%, Rubin's R of 0.5-2, and a percentage bias of <10% for each covariate.
“Nearly all analyses show Rubin's B<25%, Rubin's R of 0.5–2, and a percentage bias of <10% for each covariate (Fig. 1) (Morgan, 2018; Rubin, 2001).”
From Longitudinal associations between reading for pleasure and child maladjustment: Results from a propensity score matching analysis (2020) · cites this paper as Morgan, 2018
In the covariate-balance discussion (article pp. 404–406), Morgan describes a 10% threshold for absolute standardized differences and says all covariates in its Figure 1 example fall below it after matching. The paper does not report Rubin’s B or R thresholds, nor does it establish the citing paper’s result that nearly all analyses meet these criteria. (LLM, conf=0.86)
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References 14 · Cited by 193
Research works printed but not linked: 12
- Burns PB, Rohrich RJ, Chung KC. The levels of evidence and their role in evidence-based medicine. Plast Reconstr Surg. 2011;128:305-10.
- Nappi C, Gaudieri V, Acampa W, Assante R, Zampella E, Mainolfi C, et al. Comparison of left ventricular shape by gated spect imaging in diabetic and nondiabetic patients with normal myocardial perfusion: a propensity score analysis. J Nuclear Cardiol. 2017. doi:10.1007/s12350-017-1009-6. · DOI
- Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70:41-55.
- Rosenbaum PR, Rubin DB. Reducing bias in observational studies using subclassification on the propensity score. J Am Stat Assoc. 1984;79:516-24.
- Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivar Behav Res. 2011;46:399-424.
- Westreich D, Lessler J, Funk MJ. Propensity score estimation: machine learning and classification methods as alternatives to logistic regression. J Clin Epidemiol. 2010;63:826-33.
- Shadish WR, Steiner PM. A primer on propensity score analysis. Newborn Infant Nurs Rev. 2010;10:19-26.
- Rosenbaum PR, Rubin DB. Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. Am Stat. 1985;39:33-8.
- Mahalanobis PC. On the generalised distance in statistics. Proc Natl Inst Sci India. 1936;2:49-55.
- D'Agostino RB. Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat Med. 1998;17:2265-81.
- Joffe MM, Rosenbaum PR. Invited commentary: Propensity scores. Am J Epidemiol. 1999;150:327-33.
- Liu W, Kuramoto SJ, Stuart EA. An introduction to sensitivity analysis for unobserved confounding in non-experimental pre- vention research. Prev Sci. 2013;14:570-80.
- Authors
- Morgan, C. J.
- Affiliations
- University of Alabama at Birmingham (US)
- Corresponding author
- Morgan, Charity J.
- ORCID
- Morgan, Charity J.
- Venue
- Journal of Nuclear Cardiology
- Publisher
- Elsevier BV
- Published
- 2018-04
- Volume
- 25
- Issue
- 2
- Pages
- 404-406
- ISSN
- 1071-3581 (ISSN-L)
- OpenAlex
- W2742752821
- Cited by
- 193
- References
- 14
- Citations by year
- 2026: 15 · 2025: 22 · 2024: 36 · 2023: 27 · 2022: 27 · 2021: 27 · 2020: 24 · 2019: 13 · 2018: 2
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Nuclear Cardiology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Keywords
- propensity score matching, bias mitigation, confounding, selection bias, observational studies
- MeSH terms
- Diabetes Mellitus, Humans, Monte Carlo Method, Tomography, Emission-Computed, Single-Photon, Bias, Propensity Score
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