Home  >>  Archives  >>  Volume 13 Number 3  >>  st0306

The Stata Journal
Volume 13 Number 3: pp. 492-509

Subscribe to the Stata Journal

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
Morgen M. Miller
Departments of Health Management & Policy and Economics
University of Michigan
Ann Arbor, MI
Lawrence C. Kleinman
Departments of Health Evidence & Policy and Pediatrics
Icahn School of Medicine at Mount Sinai
New York, NY
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

Download citation: BibTeX  RIS

Download citation and abstract: BibTeX  RIS

Contact StataCorp

Contact service@stata-journal.com if you have questions about the Stata Journal.

© Copyright 2001–2014 StataCorp LP.   Terms of use.   Privacy policy.