Learn on statistical explanations for a large proportion of Phase III clinical trials failures, and how to address both the overestimation of treatment effects, and the misinterpretation of P-values via Bayesian methods.
ABSTRACT – PHASE III TRIALS FAILURES:
The clinical development of new drugs is known to be risky, with overall historical success rates from phase I to approval of less than 20%. The most critical phase of clinical development is the transition from phase II to phase III trials, with historical success rates around 40%. In other words, of all drugs that enter clinical development, 4 in 5 fail to get approved, and half of the failures occur in phase III.
- The purpose of this talk will be to investigate statistical explanations for such a large proportion of failures, including the overestimation of treatment effects in phase II trials, the “winner’s curse”, and misinterpretation of P-values.
- These ideas will be explored through an informal discussion between a statistician and the head of a clinical development program.
- Moreover, Bayesian concepts will be explained in a non-technical manner and will be shown to lead to more sensible decision-making through an attenuation of the overestimation of treatment effects, and the replacement of P-values by posterior probabilities of the null hypothesis.
KEY TAKEAWAYS:
- Phase II trials are likely to overestimate the magnitude of treatment effects
- P-values are frequently misinterpreted, which leads to a “replication crisis”
- Bayesian methods can address both the overestimation of treatment effects, and the misinterpretation of P-values
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