Biostatisticians should help shape the protocol before key design decisions are locked: endpoints, sample size, randomization, estimands and interim analyses. Those choices drive feasibility, budget, timeline and regulatory acceptability. Brought in late, a statistician inherits a design they can only document, not improve.
In emerging biotechs, statistics is often treated as an analysis function that arrives with the CRO. By then, the protocol synopsis has been circulated to investors, discussed with key opinion leaders and sometimes previewed with regulators. Changing it becomes politically and practically difficult.
What do statisticians contribute early?
Endpoints. Whether a proposed endpoint can be measured reliably and is sensitive enough to detect the effect you expect.
Estimands. Following ICH E9(R1), a precise definition of the treatment effect, including how events such as treatment discontinuation or rescue medication are handled.
Sample size. Power calculations grounded in realistic assumptions rather than hopeful ones.
Randomization and blinding. Designs that protect the integrity of the comparison.
Interim analyses. Stopping rules and adaptive elements that are statistically sound and operationally workable.
Data collection. A clear line between data the analysis needs and data that are merely interesting.
What happens when statistics arrives late?
Studies end up underpowered or larger than necessary. Endpoints prove hard to analyze. Data are collected that no analysis uses, while data that are needed were never captured. Missing data handling is improvised. The statistical analysis plan conflicts with the protocol, and regulators ask questions the team cannot answer cleanly.
Why does this matter for operations?
Statistical choices are operational choices. A smaller expected effect means a larger sample, which means more sites and a longer enrollment period. An estimand that requires following participants who stop treatment changes visit schedules, consent language, retention strategy and site training. When clinical operations and statistics design together, the protocol is more likely to be both scientifically sound and deliverable.
How can a lean sponsor get statistical input early?
Engage an experienced independent statistician for protocol design, even if the CRO will later run the analysis. Include statistical questions in your early regulatory interactions. And when the CRO's statisticians take over, hand over the design rationale, not just the protocol, so they understand why decisions were made.
How do you know you involved statistics early enough?
A simple test: could your statistician explain, without notes, why the sample size is what it is, which assumptions drive it and what would happen if enrollment produced a different population than expected? If not, their involvement started too late or ended too soon.
Keep reading: Running a Critical-to-Quality Workshop That Changes the Protocol
