Bayesian Sequential Design for Identifying and Ranking Effective Patient Subgroups in Precision Medicine in the Case...

Area of Science:

• Biostatistics
• Clinical Trial Design
• Precision Medicine

Background:

• Precision medicine addresses patient variability, necessitating advanced clinical trial designs.
• Traditional treatments may be suboptimal for specific patient subgroups.
• Biomarker-stratified trials require innovative statistical methods for complex endpoints.

Purpose of the Study:

• To develop a Bayesian sequential design for evaluating therapeutic interventions in intensive care units.
• To address complex endpoints characterized by excess zeros and right truncation.
• To rank patient subgroups based on treatment effect prediction.

Main Methods:

• A Bayesian sequential scheme was employed.
• A zero-inflated truncated Poisson model handled complex data.
• Posterior distribution of rankings and Surface Under the Cumulative Ranking curve (SUCRA) were used for subgroup evaluation.
• Interim analyses with early stopping for efficacy were incorporated.

Main Results:

• The proposed method efficiently managed complex endpoints.
• It provided a comprehensive ranking of patient subgroups.
• Simulation studies showed high accuracy in identifying the most predictive subgroup.
• Satisfactory false positive and true positive rates were achieved.

Conclusions:

• The Bayesian sequential design is effective for personalized medicine in intensive care settings.
• The method successfully manages complex endpoints and controls decision errors.
• This approach shows promise for optimizing treatment selection in challenging clinical contexts.