Optimal planning of phase II/III programs for clinical trials with multiple endpoints

Area of Science:

• Clinical Research
• Biostatistics
• Drug Development

Background:

• Drug development faces rising costs and competition, necessitating improved clinical research efficiency.
• Quantitative decision-making methods are crucial for optimizing clinical trial planning.
• Existing frameworks for phase II/III program planning are limited to single time-to-event endpoints.

Purpose of the Study:

• To generalize a utility-based framework for optimal planning of phase II/III clinical trials.
• To extend the methodology to handle multiple endpoints and normally distributed test statistics.
• To provide optimal phase II sample sizes and go/no-go decision rules for various win criteria.

Main Methods:

• Generalization of a utility-based framework for clinical trial planning.
• Application to trials with multiple endpoints and asymptotically normally distributed test statistics.
• Development of optimal sample size and decision rules for "all-or-none" and "at-least-one" win criteria.

Main Results:

• A generalized quantitative framework for phase II/III program planning with multiple endpoints.
• Optimal phase II sample sizes and go/no-go decision rules derived.
• Demonstrated applicability to Alzheimer disease and oncology drug development programs.

Conclusions:

• The proposed method offers a quantitative basis for optimizing phase II/III clinical trial planning with multiple endpoints.
• Enhances decision-making in drug development, improving efficiency and success probability.
• Provides a valuable tool for pharmaceutical research in complex therapeutic areas.