Computing adjusted risk ratios and risk differences in Stata

Edward C. Norton, Morgen M. Miller, Lawrence C. Kleinman

Research output: Contribution to journalArticlepeer-review

152 Scopus citations

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.

Original languageEnglish
Pages (from-to)492-509
Number of pages18
JournalStata Journal
Volume13
Issue number3
DOIs
StatePublished - Sep 2013

Keywords

  • Adjusted risk difference
  • Adjusted risk ratio
  • Logistic
  • Logit
  • Multinomial
  • Odds ratio
  • Ordered
  • Probit
  • Risk difference
  • Risk ratio
  • adjrr
  • st0306

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