@article{Norton:249804,
      recid = {249804},
      author = {Norton, Edward C. and Miller, Morgen M. and Kleinman,  Lawrence C.},
      title = {Computing adjusted risk ratios and risk differences in  Stata},
      journal = {Stata Journal},
      address = {2013},
      number = {199-2016-2858},
      pages = {20},
      year = {2013},
      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.},
      url = {http://ageconsearch.umn.edu/record/249804},
      doi = {https://doi.org/10.22004/ag.econ.249804},
}