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Computing adjusted risk ratios and risk differences in Stata

Author

Listed:
  • Edward C. Norton

    (University of Michigan - Ann Arbor)

  • Morgen M. Miller

    (University of Michigan - Ann Arbor)

  • Lawrence C. Kleinman

    (Icahn School of Medicine at Mount Sinai)

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. Copyright 2013 by StataCorp LP.

Suggested Citation

  • Edward C. Norton & Morgen M. Miller & Lawrence C. Kleinman, 2013. "Computing adjusted risk ratios and risk differences in Stata," Stata Journal, StataCorp LP, vol. 13(3), pages 492-509, September.
    • Handle: RePEc:tsj:stataj:v:13:y:2013:i:3:p:492-509
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    Cited by:

    1. Jose R. Rubio-Valverde & Wilma J. Nusselder & Johan P. Mackenbach, 2019. "Educational inequalities in Global Activity Limitation Indicator disability in 28 European Countries: Does the choice of survey matter?," International Journal of Public Health, Springer;Swiss School of Public Health (SSPH+), vol. 64(3), pages 461-474, April.
    2. Nyqvist,Martina Björkman & Corno,Lucia & De Walque,Damien B. C. M. & Svensson,Jakob, 2015. "Using lotteries to incentivize safer sexual behavior : evidence from a randomized controlled trial on HIV prevention," Policy Research Working Paper Series 7215, The World Bank.
    3. HwaJung Choi & Robert F Schoeni & Linda G Martin, 2016. "Are Functional and Activity Limitations Becoming More Prevalent among 55 to 69-Year-Olds in the United States?," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-14, October.
    4. J. Scott Long & Sarah A. Mustillo, 2021. "Using Predictions and Marginal Effects to Compare Groups in Regression Models for Binary Outcomes," Sociological Methods & Research, , vol. 50(3), pages 1284-1320, August.
    5. Björkman Nyqvist, Martina & Svensson, Jakob & Guariso, Andrea & Yanagizawa-Drott, David, 2016. "Effect of a micro entrepreneur-based community health delivery program on under-five mortality in Uganda: a cluster-randomized," CEPR Discussion Papers 11515, C.E.P.R. Discussion Papers.
    6. John Nunley & Nabamita Dutta, 2023. "The gender gap in schooling outcomes: A cohort study of young men and women in India," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 48(33), pages 931-944.

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