A win ratio-based framework to combine multiple clinical endpoints in exploratory basket trials.

Area of Science:

• Oncology
• Biostatistics
• Clinical Trial Design

Background:

• Basket trials accelerate oncology drug development by evaluating drugs across multiple tumor indications.
• Existing statistical methods for basket trials primarily focus on single binary endpoints.
• Bayesian hierarchical modeling and pruning-and-pooling are established methods for information borrowing across indications.

Purpose of the Study:

• To explore the application of a matched win ratio framework for basket trial design and analysis.
• To incorporate clinical priorities of multiple endpoints in early-phase oncology drug development.
• To provide a statistically robust method for Go/No-Go decisions in multi-indication trials.

Main Methods:

• Simulating control arm data for each tumor indication under heterogeneous null assumptions.
• Applying matched win ratio analysis to individual tumor indications, pooled data, and pruned pooled data.
• Evaluating the performance of the proposed win ratio-based framework through simulation studies.

Main Results:

• The proposed win ratio framework demonstrates desirable operating characteristics in simulation studies.
• The framework effectively handles multiple endpoints, reflecting clinical priorities.
• The method offers flexibility in data analysis through individual, pooled, or pruned-and-pooled approaches.

Conclusions:

• The matched win ratio framework offers a valuable statistical approach for oncology basket trials.
• This method enhances the evaluation of investigational drugs across multiple tumor types by considering multiple endpoints.
• The proposed framework supports more informed Go/No-Go decisions in early-phase drug development.