Statistical pitfalls of multiple exposures in causal observational studies and tools to address them

Abstract

ABSTRACTThe Table 2 Fallacy is an interpretation error commonly encountered in medical literature. This fallacy occurs when coefficient estimates in multivariable regression models, apart from that of the primary exposure, are interpreted as total effects on the outcome. Causal diagrams can be used to identify sets of covariates that, when adjusted for, allow for unbiased estimation and correct interpretation of multiple total effects of interest. However, proper investigation of multiple total effects requires fitting several regression models and conducting multiple inferences. As the number of inferences increases, so does the rate of a false positive finding, a phenomenon known as multiplicity. While multiple comparison procedures are recognized as a critical consideration of randomized controlled trials, opinion remains divided on their use within observational studies. This commentary highlights how multiplicity may arise alongside the Table 2 Fallacy, and how causal diagrams can be used in conjunction with multiple comparison procedures to simultaneously avoid this fallacy, control the risk of spurious findings, and further align the best practices of experimental and observational studies.