Comments on: COVID-19 Update: Why it makes sense to test the Most at Risk (and why antibody tests won’t be useful for a while)

By: Tod Newman

Tod Newman — Sun, 17 May 2020 21:17:57 +0000

In reply to Stephanie Stark. Thanks so much! That's really interesting. I hadn't considered that the false positive scores might be due to other coronaviruses. I'm guessing your test is pretty widespread now considering you're going through LabCorp?

By: Stephanie Stark

Stephanie Stark — Sun, 17 May 2020 18:31:58 +0000

The false positives come from cross reacting with Abs to ie SARS, MERS, for instance, which hardly any of us here have. At least that’s what my clinic’s Ab test has as their “disclaimer.” Our labs’s Sensitivity is 98.1% and Specificity is 98.6%. That’s from Vibrant America and we are also now using LabCorp.

By: Tod Newman

Tod Newman — Wed, 06 May 2020 00:22:28 +0000

In reply to Essie. Correct, Essie! :) This is something called Bayes Rule, a mechanism that Rev. Thomas Bayes came up with to evaluate our prior beliefs on a subject with new evidence. Sounds simple, but is the underpinning of much of what we do (and most modern statistics too). For example, my prior belief about my patient's probability of having cancer right now is something small, maybe 0.05%. But if I hear that he's over 60, I might update my belief to 5%. If I then find out that he's a smoker, my prior may go to 20%. THEN, if he tests positive for cancer on a test with a false positive rate of 2%, my belief goes way up.

By: Essie

Essie — Sun, 03 May 2020 23:54:05 +0000

this sounds the the positive predictive value and the negative predictive value of the test, which as I recall take into account the “pretest probability of disease” and when you have a grip on that you can better know what to do with your results. As you say, with symptoms for nasal RT-PCR test, or with a good story that you may actually had the disease, then we better know how to interpret lab results.