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Computing Adjusted Risk Ratios and Risk Differences in Stata

Open access
Norton, Edward C., Miller, Morgen M., Kleinman, Lawrence C.·2013·The Stata Journal: Promoting communications on statistics and Stata

Abstract

In this article, we explain how to calculate adjusted risk ratios and risk differences when reporting results from logit, probit, and related nonlinear models. Building on Stata's margins command, we create a new postestimation command, adjrr, that calculates adjusted risk ratios and adjusted risk differences after running a logit or probit model with a binary, a multinomial, or an ordered outcome. adjrr reports the point estimates, delta-method standard errors, and 95% confidence intervals and can compute these for specific values of the variable of interest. It automatically adjusts for complex survey design as in the fit model. Data from the Medical Expenditure Panel Survey and the National Health and Nutrition Examination Survey are used to illustrate multiple applications of the command.

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Metadata

Authors
Norton, E. C., Miller, M. M., & Kleinman, L. C.
Venue
The Stata Journal: Promoting communications on statistics and Stata
Published
2013-09
Volume
13
Issue
3
Pages
492-509
ISSN
  • 1536-867X (ISSN-L)
  • 1536-8734
OpenAlex
W21944932
Cited by
300
References
0
FWCI
9.46
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Computing Adjusted Risk Ratios and Risk Differences in Stata. https://app.cochat.ai/papers/10.1177/1536867x1301300304
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Norton, E. C., Miller, M. M., & Kleinman, L. C. (2013). Computing Adjusted Risk Ratios and Risk Differences in Stata. The Stata Journal: Promoting communications on statistics and Stata. https://doi.org/10.1177/1536867x1301300304

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.