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Published on: September 20, 2019
Micro-randomization trial design under operational constraints
Bryan Bunning1, Victor Ritter2, Franziska K Bishop3
1Quantitative Sciences Unit, Section of Biostatistics, Department of Medicine, Stanford University, Stanford, CA, USA; Computational Medicine, Department of Medicine, Stanford University, Stanford, CA, USA.
Contemporary Clinical Trials
|May 25, 2026
Summary
This study introduces a novel design for AI-driven digital health interventions using micro-randomization and treatment allocation policies to overcome real-world clinical constraints. Longer study durations significantly boost power, offering better efficiency for pediatric type 1 diabetes trials.
Area of Science:
- Digital Health Interventions
- Artificial Intelligence in Healthcare
- Clinical Trial Design
Background:
- Micro-randomization is key for AI-driven digital health interventions but faces real-world operational and resource challenges.
- A novel design integrating micro-randomization with treatment allocation policies is proposed, inspired by a pediatric type 1 diabetes program.
- This approach aims to mitigate constraints encountered in clinical settings.
Purpose of the Study:
- To propose and evaluate a novel design for AI-driven digital health interventions that addresses operational and resource constraints.
- To develop a simulation-based tool to support the design and power calculations for such trials.
- To provide practical design considerations for implementing micro-randomization in clinical practice.
Main Methods:
- Extensive simulation studies were conducted to evaluate the proposed design's properties.
- A simulation-based power calculator, MRThreshold, was developed to aid trial design.
- The impact of operational constraints, resource allocation, and study length on efficiency and power was assessed.
Main Results:
- Operational constraints leading to imbalanced treatment assignment negatively impacted study efficiency.
- Increasing study length demonstrated a greater positive impact on statistical power compared to increasing resources.
- A study duration extension from 16 to 40 weeks resulted in over a 50% increase in power.
- A 40-week study with 100 patients achieved 84.0% power to detect a 2% change in time in glucose control.
Conclusions:
- Thoughtful trial design necessitates careful consideration of study length, sample size, and operational capacity.
- The proposed novel design and accompanying tool effectively balance micro-randomization with treatment allocation policies under operational constraints.
- This work offers valuable insights for optimizing AI-driven digital health intervention trials in pediatric type 1 diabetes and similar settings.
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