Statistical primer: multivariable regression considerations and pitfalls

Abstract

Multivariable regression models are used to establish the relationship between a dependent variable (i.e. an outcome of interest) and more than 1 independent variable. Multivariable regression can be used for a variety of different purposes in research studies. The 3 most common types of multivariable regression are linear regression, logistic regression and Cox proportional hazards regression. A detailed understanding of multivariable regression is essential for correct interpretation of studies that utilize these statistical tools. This statistical primer discusses some common considerations and pitfalls for researchers to be aware of when undertaking multivariable regression.