Lewer and colleagues explain why risk factors for a health outcome should not be studied by putting several candidate independent variables (exposures) into a multivariable regression model and identifying which are statistically significant after mutual adjustment.

Many medical and epidemiological studies use multivariable regression to test whether several independent variables (exposures) are causal determinants of a health outcome. Where mutually adjusted regression coefficients are significant, the exposures are labelled as risk factors for the outcome. We call this study design “factors associated with.” In this article, we argue that this method is flawed due to a lack of reasoning about which variables are treated as confounders, multiple statistical testing, and post hoc interpretation of the results. In some cases, researchers use algorithmic or stepwise approaches to select exposure variables, which further exacerbates these problems. Although the results of factors associated with studies often seem reasonable, these problems mean that the method can also produce implausible results, such as dementia reducing the risk of death in patients admitted to hospital for trauma,1 diabetes reducing the risk of venous thromboembolism in the general population,2 and lack of food reducing the risk of post-traumatic stress disorder in refugees.3 Many of these studies are published every year, including in well respected journals. We argue that these studies are misleading and contribute to research waste, and the “factors associated with” method should be abandoned.