See The Number That Surprised Five Sponsors

Every enrollment plan starts as a set of assumptions dressed up as a forecast.

The patient pool is deep enough.

The eligibility criteria are workable.

The interest is there.

Each one feels solid because it matches what worked before.

Then the study opens, and the number that arrives is not the number you planned around.

The assumptions you never see are the ones that cost you

The dangerous assumption is not the one you debate. It is the one so familiar that no one thinks to check it.

A Market Feasibility Test (MFT), a patient survey run before a clinical research site opens, puts those assumptions in front of real patients and reports what they do, not what a benchmark predicts they will do.

Antidote ran that test across five studies. The pattern held every time.

3% to 4% of respondents qualified across every indication tested, despite click-through above 3% and strong stated interest. (Antidote, 2026)

Of the patients who qualified, 66% to 100% reported being very interested and willing to attend clinic visits.

The demand was never the problem.

The assumptions about who could clear the criteria were.

Why reasonable assumptions still miss

The estimates behind a forecast fail for reasons that look sound in the planning room:

• They arrive with authority. Historical data and published benchmarks feel like evidence, so teams treat them as settled instead of testing them against this protocol.
• They read as reasonable on paper. A criterion that removes most of your market can look modest in a document. The cost shows up in patient behavior, not in the wording.
• They surface late. Assumption quality goes unaudited until enrollment data forces the question, and by then the fix is a mid-study change at full cost.

What the gap costs, and who carries it

For the sponsor, an untested assumption becomes a timeline that slips and a budget that reopens after the plan was approved. For the patient, it means a study built around a requirement they cannot meet, or a step they cannot reach, even when they want in.

A motivated patient who cannot qualify is a loss on both sides of the table.

The forecast reads it as weak demand.

But it was strong demand meeting an untested criterion.

Test the assumption while it still costs little to change

Before a plan gets locked, an MFT answers the questions a forecast assumes:

• Which criteria remove the most patients. You see the qualification rate before you build a network around it.
• Whether stated interest survives the protocol. Real patients respond to your real requirements, not a proxy.
• Where the market and the protocol disagree. The signal arrives in weeks, before a site opens or a budget is spent.

Across all five tests, the same lesson repeated: the assumptions were fixable, and cheap to fix, right up until the study launched on them.

An enrollment plan is a set of predictions. An MFT turns the ones most likely to break into evidence before you fund them.