Use of Bayesian decision analysis to maximize value in patient-centered randomized clinical trials in Parkinson’s disease
Name
Use of Bayesian decision analysis to maximize value in patient-centered randomized clinical trials in Parkinson s disease.pdf
Description
Published version
Size
1.63 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Chaudhuri, Shomesh E
Ben Chaouch, Zied
Hauber, Brett
Mange, Brennan
Zhou, Mo
Christopher, Stephanie
Bardot, Dawn
Sheehan, Margaret
Donnelly, Anne
McLaughlin, Lauren
Date Issued
September 3, 2025
Journal
Journal of Biopharmaceutical Statistics
Publisher
Taylor & Francis
Citation
Chaudhuri, S. E., Ben Chaouch, Z., Hauber, B., Mange, B., Zhou, M., Christopher, S., … Lo, A. W. (2025). Use of Bayesian decision analysis to maximize value in patient-centered randomized clinical trials in Parkinson’s disease. Journal of Biopharmaceutical Statistics, 35(5), 981–1000.
Version
Final published version
Abstract
A fixed one-sided significance level of 5% is commonly used to interpret the statistical significance of randomized clinical trial (RCT) outcomes. While it is necessary to reduce the false positive rate, the threshold used could be chosen quantitatively and transparently to specifically reflect patient preferences regarding benefit–risk tradeoffs as well as other considerations. How can patient preferences be explicitly incorporated into RCTs in Parkinson’s disease (PD), and what is the impact on statistical thresholds for device approval? In this analysis, we apply Bayesian decision analysis (BDA) to PD patient preference scores elicited from survey data. BDA allows us to choose a sample size (𝑛) and significance level (𝛼) that maximizes the overall expected value to patients of a balanced two-arm fixed-sample RCT, where the expected value is computed under both null and alternative hypotheses. For PD patients who had previously received deep brain stimulation (DBS) treatment, the BDA-optimal significance levels fell between 4.0% and 10.0%, similar to or greater than the traditional value of 5%. Conversely, for patients who had never received DBS, the optimal significance level ranged from 0.2% to 4.4%. In both of these populations, the optimal significance level increased with the severity of the patients’ cognitive and motor function symptoms. By explicitly incorporating patient preferences into clinical trial designs and the regulatory decision-making process, BDA provides a quantitative and transparent approach to combine clinical and statistical significance. For PD patients who have never received DBS treatment, a 5% significance threshold may not be conservative enough to reflect their risk-aversion level. However, this study shows that patients who previously received DBS treatment present a higher tolerance to accept therapeutic risks in exchange for improved efficacy which is reflected in a higher statistical threshold.
MIT Department
Sloan School of Management
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1080/10543406.2023.2170400
