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Modified Simon's minimax and optimal two-stage designs for single-arm phase II cancer clinical trials.

ABSTRACT: Simon's two-stage design and the admissible two-stage design have been commonly used in practice for single-arm phase II clinical trials when the primary endpoint is binary. The ethical benefit of the two-stage design over the single-stage design is attained by the early termination of the trial when the treatment seems to be inactive. While Simon's optimal design is the two-stage design that minimizes the expected number of subjects under the null hypothesis, the probability of falsely declaring futility after the first stage frequently seems undesirably high. In Simon's minimax design, however, it is often the case that a high proportion of the total planned subjects are evaluated in the first stage, and thus the ethical benefit may not be achieved. In this paper, we propose modified minimax and optimal two-stage designs which guarantee not only type I and II error rates but also reasonable sample size proportions in the first stage, while maintaining the probability of falsely declaring futility under a pre-selected level. The characteristics of the modified two-stage design will be compared with those of Simon's and the admissible two-stage design. The modified minimax design yields a design that requires modest increase in 29% of cases, while the modified optimal design saves 1 to 13 subjects in 81% of cases for ? = 0.2. The modified design approach provides investigators with an alternative when the sample sizes of Simon's designs are severely unbalanced or the Type II error is unacceptably high after the first stage.

SUBMITTER: Kim J

PROVIDER: S-EPMC6611508 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Modified Simon's minimax and optimal two-stage designs for single-arm phase II cancer clinical trials.

Kim Jongphil J Schell Michael J MJ

Oncotarget 20190702 42

Simon's two-stage design and the admissible two-stage design have been commonly used in practice for single-arm phase II clinical trials when the primary endpoint is binary. The ethical benefit of the two-stage design over the single-stage design is attained by the early termination of the trial when the treatment seems to be inactive. While Simon's optimal design is the two-stage design that minimizes the expected number of subjects under the null hypothesis, the probability of falsely declarin ...[more]

PMID: 31303960

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Project description:BackgroundGo/no-go decisions after phase II and sample size chosen for phase III are usually based on phase II results (e.g., the treatment effect estimate of phase II). Due to the decision rule (only promising phase II results lead to phase III), treatment effect estimates from phase II that initiate a phase III trial commonly overestimate the true treatment effect. Underpowered phase III trials are the consequence. Optimistic findings may then not be reproduced, leading to the failure of potentially expensive drug development programs. For some disease areas these failure rates are described to be quite high: 62.5%.MethodsWe integrate the ideas of multiplicative and additive adjustment of treatment effect estimates after go decisions in a utility-based framework for optimizing drug development programs. The design of a phase II/III program, i.e., the "right amount of adjustment", the allocation of the resources to phase II and III in terms of sample size, and the rule applied to decide whether to stop or to proceed with phase III influences its success considerably. Given specific drug development program characteristics (e.g., fixed and variable per patient costs for phase II and III, probable gain in case of market launch), optimal designs with respect to the maximal expected utility can be identified by the proposed Bayesian-frequentist approach. The method will be illustrated by application to practical examples characteristic for oncological studies.ResultsIn general, our results show that the program set-ups with adjusted treatment effect estimate used for phase III planning are superior to the "naïve" program set-ups with respect to the maximal expected utility. Therefore, we recommend considering an adjusted phase II treatment effect estimate for the phase III sample size calculation. However, there is no one-fits-all design.ConclusionIndividual drug development planning for a specific program is necessary to find the optimal design. The optimal choice of the design parameters for a specific drug development program at hand can be found by our user friendly R Shiny application and package (both assessable open-source via [1]).

Dataset Information

Modified Simon's minimax and optimal two-stage designs for single-arm phase II cancer clinical trials.

Publications

Modified Simon's minimax and optimal two-stage designs for single-arm phase II cancer clinical trials.

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