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This summary is machine-generated.

This study introduces a quantitative framework for Go/No-Go decisions after proof-of-concept studies. Increasing Phase II trial sample size enhances Phase III success probability more than increasing Phase III sample size alone.

Keywords:
BayesianGo/No-Goprobability of successproof-of-concepttime-to-event

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Area of Science:

  • Clinical trial design
  • Biostatistics
  • Drug development

Background:

  • Traditional Go/No-Go decisions after proof-of-concept studies are limited by small, isolated trials.
  • Hypothesis testing in traditional methods struggles to fully assess efficacy evidence strength.

Purpose of the Study:

  • To propose a quantitative Bayesian/frequentist decision framework for Go/No-Go criteria and sample size evaluation in Phase II randomized trials.
  • To develop an integrated quantitative approach for clinical development programs considering Phase II and III trials with common time-to-event endpoints.

Main Methods:

  • Developed a quantitative Bayesian/frequentist decision framework for Go/No-Go criteria.
  • Proposed an integrated approach for Phase II and III trials, allowing a discount of Phase II data.
  • Utilized a time-to-event endpoint for analysis.

Main Results:

  • The proposed framework quantitatively integrates treatment effect uncertainty for decision-making.
  • Results confirm that increasing Phase II sample size yields a greater increase in Phase III probability of success compared to equal increases in Phase III sample size.
  • Demonstrated quantitative decision-making with a real-world example in metastatic pancreatic cancer.

Conclusions:

  • The quantitative framework offers improved Go/No-Go decision-making and sample size optimization in early-phase clinical trials.
  • Strategic increases in Phase II sample size are more impactful for overall program success than solely increasing Phase III sample size.
  • The approach provides a robust method for evaluating clinical development programs with shared endpoints.