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  3. Micro-randomization Trial Design Under Operational Constraints.
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  3. Micro-randomization Trial Design Under Operational Constraints.

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A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
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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

View abstract on PubMed

Summary
This summary is machine-generated.

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.

Keywords:
CGMDiabetesMicro-randomizationMonitoringRemoteWearables

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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.