How big is a trial, really? The enrollment numbers behind your workload

Ask someone outside the industry to picture a clinical trial and they imagine thousands of patients. Ask someone who monitors them and they picture something much smaller. The registry settles the question with real enrollment numbers, and the answer reshapes how you should think about workload, site counts, and where the heavy trials actually are.

The distribution

Across roughly 4,900 completed industry interventional trials, actual enrollment:

• Median: about 70 patients
• 25th percentile: 30
• 75th percentile: 209
• 90th percentile: 500
• Largest in the sample: over 31,000

The headline is the median. Half of completed industry trials enrolled about 70 patients or fewer. The giant trials are real, but they are the tail, not the norm. The 90th percentile is only 500 patients, which means the multi-thousand-patient study everyone pictures is rarer than 1 in 10. Most clinical research is small studies, run many at a time.

Size climbs steeply with phase

The averages hide a strong pattern by phase:

• Phase 1: median about 33 patients
• Phase 2: median about 88
• Phase 3: median about 304, with the top 10 percent above 1,000
• Phase 4: median about 138

Phase 3 is where the big numbers live, because that is where a drug has to prove itself across a large, representative population before approval. A Phase 3 trial can carry ten times the patients of the Phase 1 studies feeding into it, which is why Phase 3 work means more sites, more monitoring visits, and more data to manage per study.

Why size is a workload story

Enrollment size is a rough proxy for how heavy a study is to run. A 70-patient Phase 2 might run at a handful of sites and occupy a CRA alongside several other studies. A 2,000-patient Phase 3 spreads across dozens or hundreds of sites, each needing monitoring, and can define a CRA's entire assignment. When you take a role, "how many studies" is the wrong question by itself. "How big, and what phase" tells you the actual load, because one large Phase 3 can be more work than five small Phase 2s.

This is also why site-facing burnout does not track headcount neatly. A lean team on one enormous late-phase program can be more stretched than a larger team spread across small early-phase studies. Size is the hidden variable.

What the registry cannot tell you

The registry gives you patient counts. It does not give you site counts per CRA, visits per month, or how many concurrent studies your employer actually stacks on one person, which is where workload becomes livable or brutal. Those ratios are the real measure of the job, and they live only with the people carrying them. The salary survey collects workload alongside pay because a number means nothing without the load behind it. If you know how many studies and sites you actually carry, add it. The registry shows how big the trials are. You know how many they pile on you.