Background/Aims:

There is growing interest in the use of adaptive designs to improve the efficiency of clinical trials. We apply a Bayesian decision-theoretic model of a sequential experiment using cost and outcome data from the ProFHER pragmatic trial. We assess the model’s potential for delivering value-based research.

Methods:

Using parameter values estimated from the ProFHER pragmatic trial, including the costs of carrying out the trial, we establish when the trial could have stopped, had the model’s value-based stopping rule been used. We use a bootstrap analysis and simulation study to assess a range of operating characteristics, which we compare with a fixed sample size design which does not allow for early stopping.

Results:

We estimate that application of the model could have stopped the ProFHER trial early, reducing the sample size by about 14%, saving about 5% of the research budget and resulting in a technology recommendation which was the same as that of the trial. The bootstrap analysis suggests that the expected sample size would have been 38% lower, saving around 13% of the research budget, with a probability of 0.92 of making the same technology recommendation decision. It also shows a large degree of variability in the trial’s sample size.

Conclusions:

Benefits to trial cost stewardship may be achieved by monitoring trial data as they accumulate and using a stopping rule which balances the benefit of obtaining more information through continued recruitment with the cost of obtaining that information. We present recommendations for further research investigating the application of value-based sequential designs.

中文翻译：

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具有成本效益的临床试验设计：贝叶斯序列模型在 ProFHER 实用试验中的应用

背景/目标：

人们越来越关注使用自适应设计来提高临床试验的效率。我们使用来自 ProFHER 实用试验的成本和结果数据应用顺序实验的贝叶斯决策理论模型。我们评估该模型在提供基于价值的研究方面的潜力。

方法：

使用从 ProFHER 实用试验估计的参数值，包括进行试验的成本，我们确定试验何时可以停止，如果使用模型的基于价值的停止规则。我们使用自举分析和模拟研究来评估一系列操作特性，我们将其与不允许提前停止的固定样本量设计进行比较。

结果：

我们估计应用该模型可以提前停止 ProFHER 试验，减少约 14% 的样本量，节省约 5% 的研究预算，并产生与试验相同的技术推荐。bootstrap 分析表明，预期样本量会减少 38%，节省大约 13% 的研究预算，做出相同技术推荐决策的概率为 0.92。它还显示了试验样本量的很大程度的可变性。

结论：

试验成本管理的好处可以通过在试验数据积累时监控试验数据并使用停止规则来实现，该规则在通过持续招募获得更多信息的好处与获得该信息的成本之间取得平衡。我们提出了进一步研究调查基于价值的顺序设计的应用的建议。