Computing adjusted risk ratios and risk differences in Stata
Edward C. Norton
Departments of Health Management & Policy and Economics
University of Michigan
Ann Arbor, MI
and National Bureau of Economic Research
[email protected]
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Morgen M. Miller
Departments of Health Management & Policy and Economics
University of Michigan
Ann Arbor, MI
[email protected]
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Lawrence C. Kleinman
Departments of Health Evidence & Policy and Pediatrics
Icahn School of Medicine at Mount Sinai
New York, NY
[email protected]
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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.
View all articles by these authors:
Edward C. Norton, Morgen M. Miller, Lawrence C. Kleinman
View all articles with these keywords:
adjrr, risk ratio, adjusted risk ratio, risk difference, adjusted risk difference, odds ratio, logistic, logit, probit, multinomial, ordered
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