Statistical Design and Considerations of a Phase 3 Basket Trial for Simultaneous Investigation of Multiple Tumor Types in One Study

ASBTRACT

The discovery of numerous molecular subtypes of common cancers leads to the investigation of biomarkers potentially predictive of treatment effect of an experimental treatment in multiple histologies. However, the prevalence of a putative predictive biomarker within a histology is often low, which makes it challenging to enroll adequate number of patients in a conventional histology-based confirmatory trial. An alternative approach is to study patients with a common biomarker signature in a “basket” trial across multiple histologies. This study design has previously been used to explore experimental therapies with potentially transformative effects. We present a general design concept of a Phase 3 basket trial broadly applicable to any effective therapy. The trial is designed with scientific and statistical rigor to enable the approval of an experimental treatment in multiple tumor indications based on the outcome from a single study. Given the difficulty in indication selection, the basic idea is to prune the inactive indications at an interim analysis and pool the active indications in the final analysis. A critical statistical issue of the basket design is Type I error control for the pooled analysis after pruning. While pruning may be seen as cherry-picking which tends to inflate the Type I error, it also shares similarity with a binding futility analysis which tends to deflate the Type I error if all indications are pruned. The net impact of pruning is complicated. The use of different endpoints for pruning and pooling further complicates the issue. This paper will provide statistical details on Type I error control for the general basket design concept under three sample size adjustment strategies after pruning. Power and sample size calculations are also provided. Comparisons are made to a straightforward design without pruning. Supplementary materials for this article are available online.

KEYWORDS:

• Adaptive design
• Pharmacogenomics
• Phase III trial

Acknowledgments

Aiying Chen, a PhD candidate in statistics at Temple University, provided great programming support of this project while working as a summer intern at Merck & Co., Inc. This research is part of the collaborative work with the Drug Information Association (DIA) Small Population Work Stream under the DIA Adaptive Design Scientific Working Group. During the preparation of the article, we have received helpful comments from two anonymous referees.

Funding

Robert A. Beckman is a stockholder in Johnson & Johnson, a diversified corporation with both marketed healthcare products and healthcare products in development. Beckman is the founder and Chief Scientific Officer of Onco-mind, LLC, a company formed around potential applications of a new approach to precision medicine of cancer. This role is uncompensated. He is listed as a primary inventor on a published patent application related to my role in Onco-mind. No royalties or income are anticipated in the immediate future. He is on the Precision Medicine Advisory Board for the Cancer Institute of New Jersey.