Objective: Generalized linear models (GLMs) such as logistic and Poisson regression are among the most common statistical methods for modeling binary and count outcomes. Though single-coefficient tests (odds ratios, incidence rate ratios) are the most common way to test predictor-outcome relations in these models, they provide limited information on the magnitude and nature of relations with outcomes. We assert that this is largely because they do not describe direct relations with quantities of interest (QoIs) such as probabilities and counts. Shifting focus to QoIs makes several critical nuances of GLMs more apparent.
Method: To bolster interpretability of these models, we provide a tutorial on logistic and Poisson regression and suggestions for enhancements to current reporting practices for predictor-outcome relations in GLMs.
Results: We first highlight differences in interpretation between traditional linear models and GLMs, and describe common misconceptions about GLMs. In particular, we highlight that link functions (a) introduce nonconstant relations between predictors and outcomes and (b) make predictor-QoI relations dependent on levels of other covariates. Each of these properties causes interpretation of GLM coefficients to diverge from interpretations of linear models. Next, we argue for a more central focus on QoIs (probabilities and counts). Finally, we propose and provide graphics and tables, with sample R code, for enhancing presentation and interpretation of QoIs.
Conclusions: By improving present practices in the reporting of predictor-outcome relations in GLMs, we hope to maximize the amount of actionable information generated by statistical analyses and provide a tool for building a cumulative science of substance use disorders. (PsycInfo Database Record (c) 2022 APA, all rights reserved).