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Published on: August 19, 2025
Adaptive two-stage designs for single-arm phase IIA cancer clinical trials
1University of Medicine and Dentistry of New Jersey, Division of Biometrics, School of Public Health, and The Cancer Institute of New Jersey, New Brunswick, New Jersey 08903, USA. linyo@umdnj.edu
Abstract:
The main purpose of a phase IIA trial of a new anticancer therapy is to determine whether the therapy has sufficient promise against a specific type of tumor to warrant its further development. The therapy will be rejected for further investigation if the true response rate is less than some uninteresting level and the test of hypothesis is powered at a specific target response rate. Two-stage designs are commonly used for this situation. However, in many situations investigators often express concern about uncertainty in targeting the alternative hypothesis to study power at the planning stage. In this article, motivated by a real example, we propose a strategy for adaptive two-stage designs that will use the information at the first stage of the study to either reject the therapy or continue testing with either an optimistic or a skeptic target response rate, while the type I error rate is controlled. We also introduce new optimal criteria to reduce the expected total sample size.
Insights
This study introduces adaptive two-stage designs for anticancer therapy trials. These designs use early data to adjust trial goals, improving efficiency while controlling errors for better drug development.
Area of Science:
- Oncology
- Clinical Trial Design
- Biostatistics
Background:
- Phase IIA trials assess anticancer therapy promise for further development.
- Two-stage designs are common but face challenges with planning uncertainty.
- Concerns exist regarding targeting the alternative hypothesis and study power.
Purpose of the Study:
- To propose adaptive two-stage designs for anticancer therapy trials.
- To address uncertainty in planning alternative hypothesis targets and power.
- To control type I error rates while optimizing decision-making.
Main Methods:
- Development of an adaptive two-stage design strategy.
- Utilizing first-stage information for adaptive decision-making.
- Incorporating optimistic and skeptic target response rates.
- Introducing new optimal criteria for sample size reduction.
Main Results:
- The proposed strategy allows for adaptive adjustments based on interim data.
- Type I error rate is controlled throughout the adaptive process.
- New criteria aim to reduce the overall expected sample size.
Conclusions:
- Adaptive two-stage designs offer a flexible approach for phase IIA anticancer trials.
- The strategy enhances decision-making by incorporating early study findings.
- Optimized criteria can lead to more efficient clinical trial resource allocation.
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