Abstract
The Simon two-stage optimal design is often used for phase II cancer clinical trials. A study proceeds to the second stage unless the null hypothesis, that the true tumour response rate is below some specified value, is already accepted at the end of stage one. The conventional optimal design, for given type 1 and type 2 error rates, is the one which minimises the expected sample size under the null hypothesis. However, at least some new agents are active, and designs that explicitly address this possibility should be considered. We therefore investigate novel designs which are optimal under the alternative hypothesis, that the tumour response rate is higher than the null hypothesis value, and also designs which allow early stopping for efficacy. We make available, software for identifying the corresponding optimal and minimax designs. Considerable savings in expected sample sizes can be achieved if the alternative hypothesis is in fact true, without sample sizes suffering too much if the null hypothesis is true. We present an example discussing the merits of different designs in a practical context. We conclude that it is relevant to consider optimal designs under a range of hypotheses about the true response rate, and that allowing early stopping for efficacy is always advantageous in terms of expected sample size.
Highlights
The main aim of phase II cancer clinical trials is to evaluate the anti-tumour effect of a treatment, screening out agents that are insufficiently active and selecting active agents for future studies [1]
This paper investigates a novel definition of optimality based on the expected sample size when the alternative hypothesis, that the response rate is greater than the pre-specified value, is true
The rationale for only stopping for futility is that many novel agents do not work, and we want to minimise the number of patients exposed to an inactive drug, but in reality there are sufficient active agents to consider stopping for efficacy [2]
Summary
The main aim of phase II cancer clinical trials is to evaluate the anti-tumour effect of a treatment, screening out agents that are insufficiently active and selecting active agents for future studies [1]. The evidence to make this decision is evaluated by testing the null hypothesis that the true response rate is less than or equal to some pre-specified value. The Simon two-stage design only considers stopping for futility and the optimal design has the smallest expected sample size when the null hypothesis is true. This paper investigates a novel definition of optimality based on the expected sample size when the alternative hypothesis, that the response rate is greater than the pre-specified value, is true. The rationale for only stopping for futility is that many novel agents do not work, and we want to minimise the number of patients exposed to an inactive drug, but in reality there are sufficient active agents to consider stopping for efficacy [2]. One of the justifications given for only allowing stopping for futility in the Simon design
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