Panel · Use case · Clinical Operations
Enrollment Rate Forecast.
LiveEstimate ElicitationHow many patients per site per month will this rare-disease trial enroll once all sites are active?
Enrollment Rate Forecast is a ready-made use case for Clinical Operations. It ships under the Estimate Elicitation template.
For clinical operations leads. You get each seat's low, central and high with its anchors, the pooled range and what it is not, and what would narrow it. Claims are cited or marked as gaps.
Add it to your library to reuse from New → From template. Edit the saved copy in your library on the service form.
The decision
How many patients per site per month will this rare-disease trial enroll once all sites are active?
Who estimates
In rare diseases most sites enroll very few patients, and the rate is a count with a long tail. The seats start from registered trials in similar diseases.
| Seat | What it estimates from | Why it is at the table |
|---|---|---|
| Clinical Operations Director | Enrollment rates in comparable rare-disease trials and the share of sites that never enroll.1 | ClinicalTrials.gov records. |
| Head of Pharmacovigilance | How safety monitoring burden and visit load affect willingness to enroll. | The protocol and safety plan. |
| Global Medical Affairs Director | The size of the patient pool, referral patterns and competing trials. | Patient registries and guidelines. |
| Director Quality Assurance | How site start-up quality and training affect early enrollment. | Site quality records. |
What the elicitation does
A rate with a long tail. The report gives the typical rate and the share of sites at zero, because a mean rate hides that most of the enrollment comes from a few sites.1
Defined at a point in time. The quantity is defined once all sites are active, so it excludes start-up delays, which the report treats as a separate quantity.2
What would narrow it. Actual enrollment from the first sites, or feasibility surveys with named investigators.
Recommended length
Standard
Four angles bear on the number. Standard depth gives each a seat, with the dossier and the reasons for the spread, in about four pages.
You can pick another length on the form. The other two stay available.
Assumptions the elicitation checks
The elicitation states the assumptions each estimate rests on, and says what each gap limits. Examples for this quantity:
- The comparable trials had similar eligibility and burden.
- The patient pool is not already enrolled in competing trials.
- Sites will activate on the planned schedule.
Standards, guidance and sources
Method and anchors
The decision practice behind the memo itself is on the template page.
What gets flagged
Any rate taken from one trial, any estimate that ignores sites that never enroll, and any interval narrower than the spread across seats.
What arrives
- The quantity and how it is judged
- Evidence dossier
- Seat judgments
- Estimates side by side
- Why the seats differ
- Revised estimates after rebuttal
- The pooled range and what it is not
- What would narrow the range
What it will not do
The pool is a synthesis of AI judgments and not a calibrated distribution, and the seats are not weighted by performance. It does not narrow the range to look decisive.
It is not a feasibility study.
It is not a calibrated probability distribution. The pool is a synthesis of AI judgments, and the report says so.
It does not weight seats by performance. No calibration record exists for the AI seats.
It does not narrow the range to look decisive.
References
- [1] U.S. National Library of Medicine. ClinicalTrials.gov. Search on a stated date; results change.
- [2] Hemming, V., Burgman, M. A., Hanea, A. M., McBride, M. F. and Wintle, B. C. A practical guide to structured expert elicitation using the IDEA protocol. Methods in Ecology and Evolution, 2018;9(1):169–180. Described on the journal’s blog; the paper was not read.
Links checked 9 October 2026
These are the sources the template drew on. None of their authors or issuers has reviewed it.
About the Panel service
Each panelist answers in their own voice, then the discussion is drawn together. You get the question and who was in the room, every position in full, what they agreed on, where they split, and what follows. Disagreements are written down rather than smoothed into a consensus nobody actually holds, because the split is usually the part you needed.
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