METHODS ARTICLE Methods What’s the Risk? A Simple Approach for Estimating Adjusted Risk Measures from Nonlinear Models Including Logistic Regression

Objective. To develop and validate a general method (called regression risk analysis) to estimate adjusted risk measures from logistic and other nonlinear multiple regression models. We show how to estimate standard errors for these estimates. These measures could supplant various approximations (e.g., adjusted odds ratio [AOR]) that may di-verge, especially when outcomes are common. Study Design. Regression risk analysis estimates were compared with internal standards as well as with Mantel–Haenszel estimates, Poisson and log-binomial regres-sions, and a widely used (but flawed) equation to calculate adjusted risk ratios (ARR) from AOR. Data Collection. Data sets produced using Monte Carlo simulations. Principal Findings. Regression risk analysis accurately estimates ARR and differ-ences directly from multiple regression models, even when confounders are continuous, distributions are skewed, outcomes are common, and effect size is large. It is statistically sound and intuitive, and has properties favoring it over other methods in many cases. Conclusions. Regression risk analysis should be the new standard for presenting findings from multiple regression analysis of dichotomous outcomes for cross-sectional, cohort, and population-based case–control studies, particularly when outcomes are common or effect size is large. Key Words. Multiple regression analysis, logistic regression, nonlinear models, odds ratio, relative risk, risk adjustment, risk ratio The health services research literature fails to provide a satisfactory answer to the question: when outcomes are common (i.e., risk40.05 in the highest risk category), how does one best quantify the result of a logistic regressio