Antonia Zapf, Christian Wiessner, Inke Regina König
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The prerequisites for the use of each of these models, their applications, and their limitations are described in detail.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":11258,"journal":{"name":"Deutsches Arzteblatt international","volume":null,"pages":null},"PeriodicalIF":6.5000,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11019761/pdf/","citationCount":"0","resultStr":"{\"title\":\"Regression Analyses and Their Particularities in Observational Studies.\",\"authors\":\"Antonia Zapf, Christian Wiessner, Inke Regina König\",\"doi\":\"10.3238/arztebl.m2023.0278\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>\",\"PeriodicalId\":11258,\"journal\":{\"name\":\"Deutsches Arzteblatt international\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.5000,\"publicationDate\":\"2024-02-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11019761/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Deutsches Arzteblatt international\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.3238/arztebl.m2023.0278\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MEDICINE, GENERAL & INTERNAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Deutsches Arzteblatt international","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3238/arztebl.m2023.0278","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
Regression Analyses and Their Particularities in Observational Studies.
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.
期刊介绍:
Deutsches Ärzteblatt International is a bilingual (German and English) weekly online journal that focuses on clinical medicine and public health. It serves as the official publication for both the German Medical Association and the National Association of Statutory Health Insurance Physicians. The journal is dedicated to publishing independent, peer-reviewed articles that cover a wide range of clinical medicine disciplines. It also features editorials and a dedicated section for scientific discussion, known as correspondence.
The journal aims to provide valuable medical information to its international readership and offers insights into the German medical landscape. Since its launch in January 2008, Deutsches Ärzteblatt International has been recognized and included in several prestigious databases, which helps to ensure its content is accessible and credible to the global medical community. These databases include:
Carelit
CINAHL (Cumulative Index to Nursing and Allied Health Literature)
Compendex
DOAJ (Directory of Open Access Journals)
EMBASE (Excerpta Medica database)
EMNursing
GEOBASE (Geoscience & Environmental Data)
HINARI (Health InterNetwork Access to Research Initiative)
Index Copernicus
Medline (MEDLARS Online)
Medpilot
PsycINFO (Psychological Information Database)
Science Citation Index Expanded
Scopus
By being indexed in these databases, Deutsches Ärzteblatt International's articles are made available to researchers, clinicians, and healthcare professionals worldwide, contributing to the global exchange of medical knowledge and research.