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Using the Standardized Difference to Compare the Prevalence of a Binary Variable Between Two Groups in Observational Research

Austin, Peter C.·2009·Communications in Statistics - Simulation and Computation

Abstract

Researchers are increasingly using the standardized difference to compare the distribution of baseline covariates between treatment groups in observational studies. Standardized differences were initially developed in the context of comparing the mean of continuous variables between two groups. However, in medical research, many baseline covariates are dichotomous. In this article, we explore the utility and interpretation of the standardized difference for comparing the prevalence of dichotomous variables between two groups. We examined the relationship between the standardized difference, and the maximal difference in the prevalence of the binary variable between two groups, the relative risk relating the prevalence of the binary variable in one group compared to the prevalence in the other group, and the phi coefficient for measuring correlation between the treatment group and the binary variable. We found that a standardized difference of 10% (or 0.1) is equivalent to having a phi coefficient of 0.05 (indicating negligible correlation) for the correlation between treatment group and the binary variable.

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Metadata

Authors
Austin, P. C.
Venue
Communications in Statistics - Simulation and Computation
Published
2009-04-09
Volume
38
Issue
6
Pages
1228-1234
ISSN
  • 0361-0918 (ISSN-L)
  • 1532-4141
OpenAlex
W2049491523
Cited by
2,445
References
1
FWCI
9.02
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Using the Standardized Difference to Compare the Prevalence of a Binary Variable Between Two Groups in Observational Research. https://app.cochat.ai/papers/10.1080/03610910902859574
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Austin, P. C. (2009). Using the Standardized Difference to Compare the Prevalence of a Binary Variable Between Two Groups in Observational Research. Communications in Statistics - Simulation and Computation. https://doi.org/10.1080/03610910902859574

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