Regression Analyses and Their Particularities in Observational Studies—Part 32 of a Series on Evaluation of Scientific Publications

TY - JOUR

T1 - Regression Analyses and Their Particularities in Observational Studies—Part 32 of a Series on Evaluation of Scientific Publications

AU - Zapf, Antonia

AU - Wiessner, Christian

AU - König, Inke Regina

PY - 2024/2/23

Y1 - 2024/2/23

N2 - BACKGROUND: Regression 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.METHODS: Based 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.RESULTS: Regression 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.CONCLUSION: Researchers 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.

AB - BACKGROUND: Regression 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.METHODS: Based 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.RESULTS: Regression 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.CONCLUSION: Researchers 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.

KW - Humans

KW - Logistic Models

KW - Models, Statistical

KW - Observational Studies as Topic

KW - Regression Analysis

KW - Research Design

UR - https://pubmed.ncbi.nlm.nih.gov/38231741/

U2 - 10.3238/arztebl.m2023.0278

DO - 10.3238/arztebl.m2023.0278

M3 - SCORING: Journal article

C2 - 38231741

VL - 121

SP - 128

EP - 134

JO - DTSCH ARZTEBL INT

JF - DTSCH ARZTEBL INT

SN - 1866-0452

IS - 4

ER -