Don't Blend Your Forecast Sources. Score Them Separately.

Confidence: hypothesis. The calibration-tracking research below is drawn from published forecasting-tournament literature, not firsthand deployment. The source-scoring discipline is an argument I have not shipped at scale. Argue with it.

Most forecasting pipelines have more than one source feeding the final number: a site's self-reported capacity, a real-world-data count, a historical base rate, a vendor estimate, a model's own prediction. And most pipelines do the same thing with all of them: average them into one number and report that. The average feels rigorous. It's actually where the signal goes to die.

The clinical why

A clinical trial's enrollment forecast is built from exactly this kind of blend: what a site says it can enroll, what real-world data says the eligible population actually looks like, and what similar trials historically achieved per site per month. Each of these sources has a wildly different, well-documented failure mode. Site self-reports run optimistic. A coordinator recalls a handful of patients who might qualify and projects that recollection into a monthly commitment. Real-world-data counts are only as good as the criteria translated into a query. Historical base rates assume the new trial resembles the old ones closely enough to transfer.