Tenets of Good Practice in Regression Analysis. A Brief Tutorial

Background: Regression analysis quantifies the relationships between one or more independent variables and a dependent variable and is one of the most frequently used types of analysis in medical research. The aim of this article is to provide a brief theoretical and practical tutorial for neurosurgeons wishing to conduct or interpret regression analyses. Methods and Results: Data preparation, univariable and multivariable analysis, choice of model, model requirements and assumptions are discussed, as essential prerequisites to any regression analysis. Four main types of regression techniques are presented: linear, logistic, multinomial logistic, and proportional odds logistic. To illustrate the applications of regression to real-world data and exemplify the concepts introduced, we used a previously reported data set of patients with intracranial aneurysms treated by microsurgical clip reconstruction at the Department of Neurosurgery of Erasmus MC University Medical Center Rotterdam, between January 2000 and January 2019. Conclusions: Regression analysis is a powerful and versatile instrument in data analysis. This material is intended as a starter for those wishing to critically interpret or perform regression analysis and we recommend multidisciplinary collaborations with trained methodologists, statisticians, or epidemiologists.