One problem may be the way we teach statistics to data scientists and public health professionals. Multivariable regression is often mistaken for a silver bullet that magically controls away confounding for all variables at once, as long as no confounder is left out. This is what statisticians call the “Table 2 fallacy,” because the adjusted effect sizes in a multivariable model are so often reported in Table 2. Many medical professionals learn to read research articles critically for understanding without ever having been introduced to the Table 2 fallacy.
Confounding is often taught as a purely mathematical concept, but that misses the point. Throwing a large set of variously interrelated variables into a big stepwise regression model might be expected to work, if all you know about confounding is that you should “never leave a confounder out” of your analysis.
Author(s): Savanna Reid
Publication Date: 14 February 2021
Publication Site: towards data science