Optimal Two-Stage Designs for Phase II Cancer Trials
Author Information
Author(s): Adrian Mander, Simon Thompson
Primary Institution: MRC Biostatistics Unit, Institute of Public Health, Cambridge, United Kingdom
Hypothesis
Can optimal designs under the alternative hypothesis improve expected sample sizes in phase II cancer clinical trials?
Conclusion
Optimal designs that allow early stopping for efficacy can significantly reduce expected sample sizes in phase II cancer trials.
Supporting Evidence
- Stopping for efficacy can lead to considerable savings in expected sample sizes.
- Traditional designs often do not consider stopping for efficacy, which can be beneficial.
- Optimal designs under the alternative hypothesis can improve trial efficiency.
Takeaway
This study shows that if a cancer treatment is effective, we can design trials that use fewer patients by stopping early when the treatment works well.
Methodology
The study investigates novel two-stage designs that allow stopping for efficacy and compares them to traditional designs.
Limitations
The designs are based on specific assumptions about response rates and may not apply universally to all cancer treatments.
Digital Object Identifier (DOI)
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