The two‐trials rule for drug approval requires “at least two adequate and well‐controlled studies, each convincing on its own, to establish effectiveness.” This is usually implemented by requiring two significant pivotal trials and is the standard regulatory requirement to provide evidence for a new drug's efficacy. However, there is need to develop suitable alternatives to this rule for a number of reasons, including the possible availability of data from more than two trials. I consider the case of up to three studies and stress the importance to control the partial Type‐I error rate, where only some studies have a true null effect, while maintaining the overall Type‐I error rate of the two‐trials rule, where all studies have a null effect. Some less‐known ‐value combination methods are useful to achieve this: Pearson's method, Edgington's method and the recently proposed harmonic mean ‐test. I study their properties and discuss how they can be extended to a sequential assessment of success while still ensuring overall Type‐I error control. I compare the different methods in terms of partial Type‐I error rate, project power and the expected number of studies required. Edgington's method is eventually recommended as it is easy to implement and communicate, has only moderate partial Type‐I error rate inflation but substantially increased project power.

中文翻译：

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超越两次审判规则

药物批准的两次试验规则要求“至少进行两项充分且对照良好的研究，每项研究都有其自身的说服力，才能确定有效性。”这通常需要进行两项重要的关键试验来实现，并且是为新药疗效提供证据的标准监管要求。然而，由于多种原因，包括可能获得来自两次以上试验的数据，需要制定该规则的合适替代方案。我考虑了最多三项研究的情况，并强调控制部分 I 型错误率的重要性，其中只有一些研究具有真正的零效应，同时保持两次试验规则的总体 I 型错误率，其中所有研究均无效。一些鲜为人知的值组合方法可用于实现此目的：Pearson 方法、Edgington 方法和最近提出的调和平均值检验。我研究了它们的特性，并讨论了如何将它们扩展到对成功的连续评估，同时仍然确保总体 I 类错误控制。我从部分 I 类错误率、项目能力和预期所需研究数量方面比较了不同的方法。埃丁顿的方法最终被推荐，因为它易于实现和沟通，只有适度的部分 I 类错误率膨胀，但显着提高了项目能力。