Scott Berry convenes co-authors Kert Viele, Joe Marion, and Lindsay Berry to discuss the statistical and developmental nuances of inferentially seamless phase 2/3 clinical trial designs. The group dissects the simple method for distributing alpha when including stage 1 data, whether it is a good idea to distribute alpha, and the optimal allocation of sample size when Stage 1 data are carried forward, all referencing their recently published work in Pharmaceutical Statistics.
Key Highlights:
- Systematic definition of seamless phase 2/3 trial designs, contrasting fixed, separate-phase models with integrated, inferentially seamless approaches.
- Detailed explanation of the required alpha adjustment when selecting doses partway through—leveraging group sequential theory, normal approximations, and quadrature for explicit formula derivation; R code and calculation procedure are made available for practitioners.
- Exploration of the information fraction curve for adjusted alpha, emphasizing that initial adjustment is numerically significant but does not inherently reduce statistical power.
- Findings indicate that power is always higher when including stage 1 data – and outperforms a closed testing procedure.
- Discussion of when seamless trials may not be advantageous: operational and statistical limitations: insufficient endpoint/regulatory understanding for phase 3, differences in manufacturing readiness, need for public phase 2 results for funding, and proof of concept hurdles; identifies real scenarios where seamless 2/3 designs are challenging.
- Considerations for operational bias and blinding, with technical commentary on the boundaries of unblinding within company roles.
- Provision of practical R code and explicit analytic guidance for calculating adjusted alpha in seamless design protocols.
