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Published on: June 21, 2018
A win ratio-based framework to combine multiple clinical endpoints in exploratory basket trials
Pingye Zhang1, Xiaoyun Nicole Li1
1Global Statistics and Data Science, BeiGene, Ltd, Ridgefield Park, New Jersey, USA.
Abstract:
In contemporary exploratory phase of oncology drug development, there has been an increasing interest in evaluating investigational drug or drug combination in multiple tumor indications in a single basket trial to expedite drug development. There has been extensive research on more efficiently borrowing information across tumor indications in early phase drug development including Bayesian hierarchical modeling and the pruning-and-pooling methods. Despite the fact that the Go/No-Go decision for subsequent Phase 2 or Phase 3 trial initiation is almost always a multi-facet consideration, the statistical literature of basket trial design and analysis has largely been limited to a single binary endpoint. In this paper we explore the application of considering clinical priorities of multiple endpoints based on matched win ratio to the basket trial design and analysis. The control arm data will be simulated for each tumor indication based on the corresponding null assumptions that could be heterogeneous across tumor indications. The matched win ratio matching on the tumor indication can be performed for individual tumor indication, pooled data, or the pooled data after pruning depending on whether an individual evaluation or a simple pooling or a pruning-and-pooling method is used. We conduct the simulation studies to evaluate the performance of proposed win ratio-based framework and the results suggest the proposed framework could provide desirable operating characteristics.
Insights
This study introduces a novel win ratio framework for oncology basket trials, enhancing decision-making for investigational drugs across multiple tumor types. The proposed method improves efficiency in early-phase drug development by considering multiple endpoints.
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.
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