I wonder what are some common strategies to overcome referral \ workup bias:
Some patients might not get the full diagnosis because of existing pre-test protocol and the outcome will be labaled as Non-Event (0) even though the underlying outcome exists (1).
This is a problem when analyzing data the indirect way using sensitivity and specificity. If you are on the other hand in forward-time predictive mode where you estimate probabilities of disease on the basis of current data, I donāt think you need to do anything special with, for example, logistic regression.
The current data does not indicate the underlying outcome for some cases and the probabilities will be biased regardless of the performance metrics.
If āDavidā has a true outcome 1 and a reported outcome 0 because he didnāt do the riskier and more accurate diagnosis we must adjust for it somehow.
To within the resolution of available data, you use pre-test patient characteristics to model risk, and the predictors in the model should include indicators of what makes a patient tend to not get the ultimate test. So based on best available evidence youāve accounted for what needs to be accounted for. Unless Iām missing something.
Benās presentation is excellent. I was hoping to hear something about resistance to workup bias that results from having enough representation of patients of a certain type in the complete data. For example if females seldom go the final diagnosis but you have 100 females in your dataset who did, the model might be OK.
It is indeed an excelent presentation! I showed it to my team two days ago.
For me the most disturbing points are 8 for diagnosis (this thread) and point 9 for prognosis that leads me to counterfactual predictions.
I sent Ben email asking about the subject and he sent me some links to work done by Joris A H de Groot and a related tutorial in R: