A Unified Approach to Flexible Sample Size Design with Realistic Constraints

Abstract

Flexible sample size designs based on interim efficacy results can ensure adequate power by adjusting the sample size, which potentially saves time and resources. However, the Type I error can often be inflated due to such adjustments. We use a unified approach to quantify the Type I error rate and to adjust the stopping boundary accordingly to maintain the overall Type I error. This unified approach can be applied to normal, survival, and binary endpoints. Several aspects of sample size adjustments are considered based on information time. The Type I error inflation can be well controlled by giving up some unrealistic power. Simulations show the proposed method works well for survival and binary endpoints.

Keywords: :

• Adaptive design
• Brownian motion
• Futility
• Group sequential
• Sample size reestimation
• Stopping boundary
• Type I error