## Number Needed to Treat (NNT): Just Another Magic Number?

Excerpt:

Nomograms are a trending term in evidence-based medicine, and COVID-19 research is no exception. In this context, a nomogram is usually a web-based tool, a graphic interface, or an on-line calculator in which patient data on several variables is entered as input, and a single summary statistic is calculated as output, such as the likelihood of successful response to treatment. Many medical researchers and data scientists have put forward nomograms derived from multivariate clinical progression models, to assist in decisions about COVID-19 triage.

Is this enthusiasm for reducing complex clinical decisions to the use of multivariate calculators a leap forward in personalized medicine, enabled by modern computing? There is a sketchy “black box” side to all this, to say nothing of the risk of incorporating statistical design errors or untenable inferential claims into a nomogram being rolled out for immediate, untested use in the middle of pandemic. So let’s treat the history of the “number needed to treat” as a “teachable moment” in the history of nomograms in medicine. What have we learned so far?

Author(s): Savanna Reid

Publication Date: 26 February 2021

Publication Site: Towards Data Science

## Table 2 fallacy and stepwise regression

Excerpt:

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