Diagnostics

Using an online dating analogy

Likey or no likey: process. Show one ‘hottie’, one ‘nottie’

Ultimately, they may be ‘hot’ or they may be ‘not hot’ when you actually meet them – more pics

2x2 table for Tinder

What are you interested in? PPV and NPV

But is that all you want to know?

Coin toss analogy in low/high prevalence cohorts (bunnyburners.com and richmusclyheroes.com)

Sensitivity and specificity: What God knows

Pooling both perspectives: likelihood ratios

Bayesian theory in practice

ROC curves: let’s say that (instead of swiping yes or no) you rate the profiles out of 100

Where do you put the cut-off? Examples

ROC curve: show and explain

Talk about AUC. Advantages (global measure)

Disadvantages of AUC: show odd shaped ROC curve

Where do you set the cut-off? Show

• Also, what do you do next? Validate in another cohort as performance calibrated to yours

Summary