ArticlePsychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors2022
Making sense of some odd ratios: A tutorial and improvements to present practices in reporting and visualizing quantities of interest for binary and count outcome models.
Article in Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 30 citations in OpenAlex.
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Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
objectiveGeneralized 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.
methodTo 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.
resultsWe 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.
conclusionsBy 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).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.