Optimization of Adaptive Designs: Efficiency Evaluation

Abstract

The rising cost of clinical trials is impeding the development of new drugs. There is an acute need for critical evaluation and innovate thinking while designing the trial. Adaptive design has been repeatedly called upon in the last decade as one of the prescriptions for this intricate problem. From a pure statistical perspective, the adaptive design framework depends heavily on the appropriate selection of the type of test statistics and stopping boundaries. There are several methods proposed in the literature, based on different test statistics and stopping boundaries. All of these methods are rigorous in controlling type I error. In this paper, we group combination p-value methods into major categories along with their stopping boundaries. We review and compare these methods based on their operating characteristics, including average sample size and maximum sample size under null and alternative hypothesis, power, and early stopping probabilities. The optimal interim analysis timing and alpha spending function were used as the independent factors for this assessment. We propose an evaluation matrix and establish a framework to assess the most efficient design in order to assist in “one stop shopping.”

Key Words:

• Adaptive design
• Interim analysis
• Power and type I error
• Sample size reestimation

Notes

Note. α₂ = β₁; *Inf, information at interim analysis.

Note. β₁ = 1; *Inf, information at interim analysis.

Note. β₁ = 1; *Inf, information at interim analysis.

Note. β₁ = 1; *Inf, information at interim analysis.

Note. β₁ = 1 and nfinal =200; *Inf, information at interim analysis.

Note. β₁ = 1 and nfinal =200; *Inf, information at interim analysis.

Note. β₁ = 1 and nfinal =200; *Inf, information at interim analysis.

Note. β₁ = 1 and nfinal =200; *Inf, information at interim analysis.

Note. α₂ = β₁; *Inf, information at interim analysis.

Note. β₁ = 1 and nfinal =200; *Inf, information at interim analysis.

Note. β₁ = 1 and nfinal =200; *Inf, information at interim analysis.

Note. β₁ = 1 and nfinal =200; *Inf, information at interim analysis.

Note. β₁ = 1 and nfinal =200; *Inf, information at interim analysis.

Note. β₁ = 1 and nfinal =200; *Inf, information at interim analysis.

Note. β₁ = 1 and nfinal =200; *Inf, information at interim analysis.

Note. β₁ = 1 and nfinal =200; *Inf, information at interim analysis.