The Importance of Making Assumptions in Bias Analysis

Quantitative bias analyses allow researchers to adjust for uncontrolled confounding, given specification of certain bias parameters. When researchers are concerned about unknown confounders, plausible values for these bias parameters will be difficult to specify. Ding and VanderWeele developed bounding factor and E-value approaches that require the user to specify only some of the bias parameters. We describe the mathematical meaning of bounding factors and E-values and the plausibility of these methods in an applied context. We encourage researchers to pay particular attention to the assumption made, when using E-values, that the prevalence of the uncontrolled confounder among the exposed is 100% (or, equivalently, the prevalence of the exposure among those without the confounder is 0%). We contrast methods that attempt to bound biases or effects and alternative approaches such as quantitative bias analysis. We provide an example where failure to make this distinction led to erroneous statements. If the primary concern in an analysis is with known but unmeasured potential confounders, then E-values are not needed and may be misleading. In cases where the concern is with unknown confounders, the E-value assumption of an extreme possible prevalence of the confounder limits its practical utility.

Automated summary

Quantitative bias analyses can help researchers adjust for uncontrolled confounding by specifying bias parameters, but specifying these parameters can be challenging when unknown confounders are a concern. The bounding factor and E-value approaches by Ding and VanderWeele only require the user to specify some bias parameters, but caution is needed when assuming extreme prevalence of the uncontrolled confounder in E-values. It is important to distinguish between biases or effects when using these methods and to consider the practical utility of E-values when dealing with unknown confounders.