Seamless 2/3 Trial Designs

What this episode covers

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.

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