A Two-Stage Covariate-Adjusted Response-Adaptive Enrichment Design

Area of Science:

• Clinical trial design
• Precision medicine
• Biostatistics

Background:

• Identifying patient subgroups that benefit most from treatment is crucial in precision medicine.
• Traditional clinical trials often assume no treatment-by-covariate interactions, leading to inefficient enrollment and unnecessary treatment for some patients.

Purpose of the Study:

• To propose a novel two-stage enrichment design for clinical trials.
• To improve trial efficiency and patient outcomes by identifying and focusing on subgroups with the greatest treatment benefit.

Main Methods:

• Utilizes covariate-adjusted response-adaptive (CARA) allocation for patient enrollment.
• Incorporates a pseudo-randomization test in interim analysis to detect treatment-by-covariate interactions.
• Employs a pre-defined alpha threshold to decide on subgroup recruitment for the second stage.

Main Results:

• The design allows for adaptation based on interim analysis findings, focusing on the best-performing subgroup if a significant interaction is detected.
• Evaluates different CARA procedures based on type I error rates, power, and ethical considerations.
• Selects the CARA procedure that optimally balances trial efficiency and ethical considerations.

Conclusions:

• The proposed two-stage enrichment design enhances precision medicine by enabling early identification of treatment-effect subgroups.
• This adaptive approach optimizes resource allocation and minimizes patient exposure to ineffective treatments.
• The study provides a framework for more efficient and ethical clinical trial conduct.