The Table 2 Fallacy and Overfitting: A Persistent Problem in Contemporary Research?

The “Table 2 fallacy” represents a common methodological error in medical research, characterized by indiscriminate statistical adjustment for multiple variables without considering their causal nature. This article examines the theoretical foundations of the problem, distinguishing between studies with descriptive, predictive, and explanatory objectives, and emphasizing how the research purpose should determine the adjustment strategy. We highlight the fundamental role of Directed Acyclic Graphs (DAGs) in correctly identifying confounding, mediating, and colliding variables, thus avoiding overadjustment and resulting biases. To illustrate these considerations, we present two practical examples: the relationship between obesity and colorectal cancer, and between coffee consumption and breast cancer. In the first case, we demonstrate how adjustment for intestinal dysbiosis (a mediator) can attenuate the association between obesity and colorectal cancer, reducing the adjusted relative risk from 1.78 (95% CI: 1.20–2.65) to 1.49 (95% CI: 0.97–2.29) and eliminating statistical significance (p=0.072). In the second example, we show how including insomnia (a collider) in the model can create artificial associations between coffee consumption and breast cancer, dramatically increasing the adjusted relative risk to 1.94 (95% CI: 1.34-2.81) with high statistical significance (p<0.001) when a correctly specified model shows no such association. We conclude that, in explanatory studies, it is essential to develop causal reasoning prior to statistical analysis, using DAGs to guide the selection of adjustment variables. This rigorous methodological approach prevents both the dilution of real causal effects and the generation of spurious associations, increasing the internal validity of epidemiological findings and their utility for clinical decision-making.