Patient-Centered Clinical Trial Design for Heart Failure Devices via Bayesian Decision Analysis

Area of Science:

• Decision Analysis
• Biostatistics
• Medical Device Regulation

Background:

• Current clinical trial significance thresholds (e.g., 2.5% false-positive rate) are fixed, irrespective of disease severity or patient values.
• Qualitative assessment of clinical significance, including patient preferences, often conflicts with quantitative statistical evidence.

Purpose of the Study:

• To apply Bayesian decision analysis to heart failure device trials to determine optimal statistical significance thresholds.
• To maximize patient utility by integrating clinical significance and patient preferences into statistical decision-making for trial design and interpretation.

Main Methods:

• Utilized discrete-choice experiment data from heart failure patients on risk-benefit trade-offs for hypothetical medical devices.
• Estimated patient utility loss from false-positive and false-negative trial outcomes.
• Calculated the Bayesian decision analysis-optimal significance threshold to maximize expected patient utility in a hypothetical randomized controlled trial.

Main Results:

• The optimal significance threshold in a baseline analysis for a 600-patient/arm trial was 3.2% (83.2% statistical power), reflecting patient willingness to accept risks for potential benefits.
• Optimal thresholds may decrease below 2.5% for higher device risks or in risk-averse patient subgroups.
• An interactive tool is provided to explore how patient preferences and parameters influence the optimal threshold.

Conclusions:

• Bayesian decision analysis offers a systematic, transparent, and repeatable method for integrating clinical and statistical significance.
• This approach explicitly incorporates disease burden and patient preferences into regulatory decision-making for medical devices.