ideasIdea
Agree in advance how to borrow evidence between similar rare cancers
Statistical methods can combine information across similar rare cancers to reach an answer with fewer patients. Regulators need to say in advance when that is acceptable.
Hierarchical Bayesian models with borrowing across histologies or across related rare diseases can substantially reduce required sample size, and are used in basket trial analyses. Sponsors avoid them because acceptance is uncertain at review. A published position on acceptable borrowing structures, prior specification and pre-registration requirements would let sponsors design smaller trials with confidence.
Hypothesis
Regulatory clarity on borrowing methods reduces the median sample size of rare cancer registrational trials by a third without increasing the rate of subsequent effect reversals in confirmatory data.
Rationale
Similar clarity on adaptive designs and on external control arms rapidly changed practice once guidance existed. The statistical machinery is mature; the missing element is a pre-agreed acceptability boundary.
What would test it
Simulation study across historical rare cancer datasets to quantify type I error and bias under candidate borrowing structures, submitted jointly by academic statisticians and regulators as the basis for guidance.
Maturity
speculative
Who has to act
regulator
Cost to try
Small (under $1M)
Years to first evidence
4
Bottlenecks it attacks
- Rare and paediatric cancers without markets · Taken together rare cancers are a fifth of all cancers, but each one alone is too small for a company to invest in.
- Trial design, endpoints and cost · A phase 3 trial takes years and hundreds of millions of dollars, and often answers a question that has already moved on.
- Regulatory divergence between regions · Regulatory divergence means a drug approved in one country can take years to reach another, or never arrive.