Natalie E. Dean, PhD+ Your Authors @nataliexdean Assistant Professor of Biostatistics at @UF specializing in emerging infectious diseases and vaccine study design. @HarvardBiostats PhD. Tweets my own. She/her. Apr. 21, 2020 1 min read + Your Authors

A toy example of why test sensitivity and specificity matter in serosurveys.

Imagine population seroprevalence is 4%. Test sensitivity is 80%. Specificity is 99.9%. For a random sample of 3000 participants, you would expect ~100 positives, 3% of which will be false positives.

Now imagine that seroprevalence is 2% (lower). Test sensitivity is 90% (higher). Specificity is 98.5% (lower). For a random sample of 3000 participants, you would expect ~100 positives, 45% of which will be false positives.

Same number of positives, different seroprevalence.

How do we whether we are in the 2% or 4% seroprevalence setting? It depends on the sensitivity/specificity of the test. But the tests are so new, that we aren't sure how well they work. Which is why the safest conclusion is that seroprevalence is "low," no more precise than that.

Want to learn more about sensitivity and specificity? My thread here.

Also, a public apology for making people look at grainy screenshots of R code!

Lots of nice tutorials out there on this topic. 

You can follow @nataliexdean.


Tip: mention @threader_app on a Twitter thread with the keyword “compile” to get a link to it.

Enjoy Threader? Sign up.

Since you’re here...

... we’re asking visitors like you to make a contribution to support this independent project. In these uncertain times, access to information is vital. Threader gets 1,000,000+ visits a month and our iOS Twitter client was featured as an App of the Day by Apple. Your financial support will help two developers to keep working on this app. Everyone’s contribution, big or small, is so valuable. Support Threader by becoming premium or by donating on PayPal. Thank you.

Follow Threader