We would like to make the readership aware that risk or prevalence ratios and differences, when they are the parameter of interest, can be directly calculated by using SAS software (SAS Institute, Inc., Cary, North Carolina). There is no longer any good justification for fitting logistic regression models and estimating odds ratios when the odds ratio is not a good approximation of the risk or prevalence ratio. Instead, SAS PROC GENMOD's log-binomial regression (1) capability can be used for estimation and inference about the parameter of interest. Here is an example of the code required to analyze the breast cancer survival data discussed by Greenland (2):from which the multivariate-adjusted risk ratios are 1.5583 (95 percent confidence interval: 1.0487, 2.3155), 2.5382 (95 percent confidence interval: 1.1734, 5.4903), and 5.8680 (95 percent confidence interval: 2.7458, 12.5406) for receptor, stage2, and stage3, respectively. The results from the SAS output are given without rounding to allow replication by the reader.