title: Representation of exposures in regression analysis and interpretation of regression coefficients: basic concepts and pitfalls.
abstract: Regression models are being used to quantify the effect of an exposure on an outcome, while adjusting for potential confounders. While the type of regression model to be used is determined by the nature of the outcome variable, e.g. linear regression has to be applied for continuous outcome variables, all regression models can handle any kind of exposure variables. However, some fundamentals of representation of the exposure in a regression model and also some potential pitfalls have to be kept in mind in order to obtain meaningful interpretation of results. The objective of this educational paper was to illustrate these fundamentals and pitfalls, using various multiple regression models applied to data from a hypothetical cohort of 3000 patients with chronic kidney disease. In particular, we illustrate how to represent different types of exposure variables (binary, categorical with two or more categories and continuous), and how to interpret the regression coefficients in linear, logistic and Cox models. We also discuss the linearity assumption in these models, and show how wrongly assuming linearity may produce biased results and how flexible modelling using spline functions may provide better estimates.
authors.affiliation: University of Bordeaux, ISPED, Centre INSERM U897-Epidemiology-Biostatistics, Bordeaux, France.
authors.forename: Karen
authors.initials: K
authors.lastname: Leffondré
authors.affiliation: ERA-EDTA Registry, Department of Medical Informatics, Academic Medical Center, Amsterdam, the Netherlands.
authors.forename: Kitty J
authors.initials: KJ
authors.lastname: Jager
authors.affiliation: University of Bordeaux, ISPED, Centre INSERM U897-Epidemiology-Biostatistics, Bordeaux, France.
authors.forename: Julie
authors.initials: J
authors.lastname: Boucquemont
authors.affiliation: ERA-EDTA Registry, Department of Medical Informatics, Academic Medical Center, Amsterdam, the Netherlands.
authors.forename: Vianda S
authors.initials: VS
authors.lastname: Stel
authors.affiliation: Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Vienna, Austria.
authors.forename: Georg
authors.initials: G
authors.lastname: Heinze
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D015415:Biomarkers / Q000378:metabolism; D015992:Body Mass Index; D015331:Cohort Studies; D003627:Data Interpretation, Statistical; D005260:Female; D005919:Glomerular Filtration Rate; D006801:Humans; D016014:Linear Models; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D016016:Proportional Hazards Models; D012044:Regression Analysis; D051436:Renal Insufficiency, Chronic / Q000453:epidemiology* / Q000378:metabolism; D012737:Sex Factors
chemical_list: D015415:Biomarkers
keywords: categorical variable; continuous variable; exposure modelling; linearity; regression