• Antonia Zapf, Christian Wiessner, and Inke Regina König.
• Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany; Institute of Medical Biometry and Statistics, University of Lübeck, Lübeck, Germany.
• Dtsch Arztebl Int. 2024 Feb 23; 121 (4): 128134128-134.

BackgroundRegression analysis is a standard method in medical research. It is often not clear, however, how the individual components of regression models are to be understood and interpreted. In this article, we provide an overview of this type of analysis and discuss its special features when used in observational studies.MethodsBased on a selective literature review, the individual components of a regression model for differently scaled outcome variables (metric: linear regression; binary: logistic regression; time to event: Cox regression; count variable: Poisson or negative binomial regression) are explained, and their interpretation is illustrated with respect to a study on multiple sclerosis. The prerequisites for the use of each of these models, their applications, and their limitations are described in detail.ResultsRegression analyses are used to quantify the relation between several variables and the outcome variable. In randomized clinical trials, this flexible statistical analysis method is usually lean and prespecified. In observational studies, where there is a need to control for potential confounders, researchers with knowledge of the topic in question must collaborate with experts in statistical modeling to ensure high model quality and avoid errors. Causal diagrams are an increasingly important basis for evaluation. They should be constructed in collaboration and should differentiate between confounders, mediators, and colliders.ConclusionResearchers need a basic understanding of regression models so that these models will be well defined and their findings will be fully reported and correctly interpreted.

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