Adaptive Stagger: Bayesian Phase 1–2 Clinical Trial Design for Patient Staggering

Bayesian Phase 1–2 Designs with Adaptive Rules for Staggering Patient Entry is a methodological dataset by Shuqi Wang, last updated on June 4, 2026. It describes and simulates the 'Adaptive Stagger' design for first-in-human dose-finding trials, which uses a utility function to trade off dose-limiting toxicities and patient response. The design is illustrated with a trial of CD70 natural killer cells for treating hematologic malignancies.

Use Cases

• Simulating trial safety and duration based on the described adaptive within-cohort staggering rules.
• Comparing optimal dose selection rates against designs with strict staggering rules as mentioned in the description.
• Modeling the tradeoff between dose-limiting toxicities and response using the described utility function.
• Designing trials that account for disease progression risk and low-grade toxicities as key features.

Strengths

• The methodology is illustrated with a concrete trial example for CD70 natural killer cells.
• The design is supported by simulation results comparing safety, dose selection, and trial duration.
• Dataset is openly licensed under CC-BY-4.0.

Limitations

• Row count and column-level documentation are unknown, which limits suitability assessment.
• The primary data format is a 3.4 MB PDF, which may require extraction of underlying simulation data.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Shuqi Wang via figshare

Collection Method

Methodological research and simulation for clinical trial design.

Freshness

Last updated 2026-06-04 16:09:03; freshness should be verified.