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BUDS: Benchmark Uncertainty Design Selection for Two-Stage Single-Arm Phase II Trials
Rebecca Irlmeier1,2, Zhuoli Jin3,2, Fei Ye1,2
1Biostatistics and Bioinformatics Shared Resource, Sylvester Comprehensive Cancer Center, Miami, Florida.
Medrxiv : the Preprint Server for Health Sciences
|June 4, 2026
Summary
New Interval-Null Robust (INR) two-stage designs address uncertainty in historical benchmarks for Phase II trials. INR designs control Type I error across a range of null values, reducing false positives from benchmark misspecification.
Area of Science:
- Clinical Trials Methodology
- Biostatistics
- Drug Development
Background:
- Traditional two-stage designs for Phase II trials rely on a fixed null benchmark.
- Incorrect specification of this benchmark, due to real-world inconsistencies, can inflate Type I error rates.
- This leads to premature advancement of ineffective treatments.
Purpose of the Study:
- To introduce the Interval-Null Robust (INR) two-stage design framework.
- To address uncertainty in historical null benchmarks for both binary and time-to-event endpoints.
- To control Type I error uniformly across a plausible range of null values.
Main Methods:
- Defined the null hypothesis as an interval of clinically uninteresting values (e.g., p ∈ [p0L, p0U]).
- Controlled Type I error uniformly over this interval, using robust criteria for design selection.
- Developed the framework for both binary and time-to-event endpoints.
Main Results:
- Classic designs showed significant Type I error inflation when the true null parameter deviated from the assumed benchmark.
- INR designs maintained nominal Type I error rates across the entire null interval.
- Robust optimization criteria managed the robustness-efficiency trade-off while preserving Type I error control.
Conclusions:
- INR two-stage designs provide a transparent method for handling historical control uncertainty in single-arm Phase II trials.
- By using a null value interval, INR designs mitigate the risk of false positives due to benchmark misspecification.
- The INR framework is applicable to various endpoints and supported by the INRDesign R package and Shiny app.
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