Quantitative decision-making in randomized Phase II studies with a time-to-event endpoint

ABSTRACT

One of the most critical decision points in clinical development is Go/No-Go decision-making after a proof-of-concept study. Traditional decision-making relies on a formal hypothesis testing with control of type I and type II error rates, which is limited by assessing the strength of efficacy evidence in a small isolated trial. In this article, we propose a quantitative Bayesian/frequentist decision framework for Go/No-Go criteria and sample size evaluation in Phase II randomized studies with a time-to-event endpoint. By taking the uncertainty of treatment effect into consideration, we propose an integrated quantitative approach for a program when both the Phase II and Phase III trials share a common endpoint while allowing a discount of the observed Phase II data. Our results confirm the argument that an increase in the sample size of a Phase II trial will result in greater increase in the probability of success of a Phase III trial than increasing the Phase III trial sample size by equal amount. We illustrate the steps in quantitative decision-making with a real example of a randomized Phase II study in metastatic pancreatic cancer.

KEY WORDS:

• Bayesian
• Go/No-Go
• probability of success
• proof-of-concept
• time-to-event