The information mismatch, and how to fix it

Abstract

We live in unprecedented times in terms of our ability to use evidence to inform medical care. For example, we can perform data-driven post-test probability calculations. However, there is work to do. As has been previously noted, sensitivity and specificity, which play a key role in post-test probability calculations, are defined as unadjusted for patient covariates. In light of this, there have been multiple recommendations that sensitivity and specificity be adjusted for covariates. However, there is less work on the downstream clinical impact of unadjusted sensitivity and specificity. We discuss this here. We argue that unadjusted sensitivity and specificity, when mixed with covariate-dependent pre-test probability scores (which are more easily available nowadays given the multitude of online calculators), can lead to a post-test probability that contains an ``information mismatch.'' We write the equations behind such an information mismatch and discuss the steps that can be taken to fix it.