Adaptive two-stage designs in phase II clinical trials.
Two-stage designs have been widely used in phase II clinical trials. Such designs are desirable because they allow a decision to be made on whether a treatment is effective or not after the accumulation of the data at the end of each stage. Optimal fixed two-stage designs, where the sample size at each stage is fixed in advance, were proposed by Simon when the primary outcome is a binary response. This paper proposes an adaptive two-stage design which allows the sample size at the second stage to depend on the results at the first stage. Using a Bayesian decision-theoretic construct, we derive optimal adaptive two-stage designs; the optimality criterion being minimum expected sample size under the null hypothesis. Comparisons are made between Simon's two-stage fixed design and the new design with respect to this optimality criterion.
Banerjee
Anindita
A
Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA. abanerj2@ncsu.edu
Tsiatis
Anastasios A
AA
D017322:Clinical Trials, Phase II as Topic / Q000379:methods*; D006801:Humans; D012107:Research Design; D018401:Sample Size