Information borrowing in Bayesian clinical trials: choice of tuning parameters for the robust mixture prior

2024-12-04

AI Summary

External data borrowing in Bayesian clinical trials is prone to bias, compromising statistical validity and estimation reliability. Method: This study systematically investigates the impact of four tuning parameters—mixture weight, location, scale, and distributional form—in robust mixture priors on operating characteristics under single-arm and hybrid control designs, via theoretical analysis and extensive simulation. Contribution/Results: We identify the location parameter as a key driver of Type I error inflation and estimation bias, and uncover strong coupling between weight and location/scale parameters. Building on sensitivity quantification and error trade-off analysis, we propose principled guidelines for parameter selection and introduce more robust alternative distributional forms. The results yield actionable, empirically validated parameter configurations that substantially enhance the statistical robustness and estimation accuracy of external data borrowing in Bayesian clinical trial design.

Technology Category

Machine Learning: Bayesian LearningIntelligent Robots: State EstimationSearch and Optimization: Mixed Discrete/Continuous Search

Application Category

User Modeling, Personalization and Recommendation: Practical large-scale studies of user experienceWeb Mining and Content Analysis: Robustness and generalizability of Web mining methodsGraph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

External data borrowing in clinical trial designs has increased in recent years. This is accomplished in the Bayesian framework by specifying informative prior distributions. To mitigate the impact of potential inconsistency (bias) between external and current data, robust approaches have been proposed. One such approach is the robust mixture prior arising as a mixture of an informative prior and a more dispersed prior inducing dynamic borrowing. This prior requires the choice of four quantities: the mixture weight, mean, dispersion and parametric form of the robust component. To address the challenge associated with choosing these quantities, we perform a case-by-case study of their impact on specific operating characteristics in one-arm and hybrid-control trials with a normal endpoint. All four quantities were found to strongly impact the operating characteristics. As already known, variance of the robust component is linked to robustness. Less known, however, is that its location can have severe impact on test and estimation error. Further, the impact of the weight choice is strongly linked with the robust component's location and variance. We provide recommendations for the choice of the robust component parameters, prior weight, alternative functional form for this component and considerations for evaluating operating characteristics.

Problem

• Optimizing robust mixture prior parameters for Bayesian clinical trials
• Addressing external data bias through dynamic borrowing mechanisms
• Evaluating parameter impacts on operating characteristics in trials

Innovation

• Robust mixture prior combining informative and dispersed distributions
• Dynamic borrowing approach mitigating external data bias
• Case-specific parameter optimization for operating characteristics