The odds ratio is a frequently used effect measure for two independent binomial proportions. Unfortunately, the confidence intervals that are available for it in Stata and other standard software packages are generally wider than necessary, particularly for small-sample and exact estimation. The...

The risk ratio can be a useful statistic for summarizing the results of cross-sectional, cohort, and randomized trial studies. I discuss several methods for estimating adjusted risk ratios and show how they can be executed in Stata, in- cluding 1) Mantel – Haenszel and inverse-variance...

Matching is occasionally used in cohort studies; examples include studies of twins and some studies of traffic crashes. Analysis of matched cohort data is not discussed in many textbooks or articles and is not mentioned in the Stata manuals. Risk ratios can be estimated using matched-pair cohort...

Logistic regression is perhaps the most widely used method for adjustment of confounding in epidemiologic studies. Its popularity is understandable. The method can simultaneously adjust for confounders measured on different scales; it provides estimates that are clinically interpretable; and its...

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...

This paper describes how to use the command hapipf and introduces the command profhap written for Stata that analyzes population-based genetic data. For these studies, association can be linkage disequilibrium within a set of loci or allelic/haplotype association with disease status. Confidence...