Data-driven treatment selection for seamless phase II/III trials incorporating early-outcome data

Area of Science:

• Clinical Trials Methodology
• Biostatistics
• Pharmaceutical Research

Background:

• Seamless phase II/III clinical trials involve a two-stage design with treatment selection at the interim analysis.
• Interim analysis often utilizes early outcome data from a larger patient cohort for treatment selection when primary endpoints require extended follow-up.

Purpose of the Study:

• To propose a new, adaptive approach for treatment selection in seamless phase II/III trials.
• To enhance the probability of correctly identifying the most effective experimental treatment using interim data.
• To compare the performance of the new method against existing approaches.

Main Methods:

• The proposed method leverages interim analysis data to estimate key parameters.
• It adaptively selects the treatment selection strategy with the highest probability of correctly identifying the superior treatment.
• Builds upon and integrates elements from two previously established methods.

Main Results:

• The new approach demonstrates robust performance across a wide range of true parameter values.
• It is shown to be competitive with, and in some instances superior to, existing methods.
• The method effectively utilizes early outcome data for informed treatment selection.

Conclusions:

• The proposed adaptive method offers an improved strategy for treatment selection in seamless clinical trials.
• This approach enhances trial efficiency and the likelihood of advancing the most promising treatments.
• The findings support the adoption of this novel method for optimizing clinical trial design.