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 employed 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 3 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 $p$-value combination methods are useful to achieve this: Pearson's method, Edgington's method and the recently proposed harmonic mean $\chi^2$-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.

翻译：药品审批的两项试验规则要求“至少有两项充分且良好对照的研究，每项研究本身均具有说服力，以确证有效性”。通常这需要两项具有显著性的关键试验，并且是新药疗效证明的标准监管要求。然而，由于多项原因（包括可能获得超过两项试验的数据），有必要制定该规则的合适替代方案。本文考虑最多三项研究的情形，强调在维持两项试验规则的整体I类错误率（所有研究均为零效应）的同时，控制部分I类错误率（仅部分研究存在真实零效应）的重要性。一些鲜为人知的$p$值组合方法可用于实现此目标：Pearson方法、Edgington方法以及最近提出的调和均值$\chi^2$检验。本文探讨了这些方法的特性，并讨论了如何将其扩展为顺序评估成功的方法，同时仍确保整体I类错误控制。通过比较不同方法的部分I类错误率、项目统计功效以及所需研究的预期数量，最终推荐Edgington方法，因其易于实施和沟通，虽导致部分I类错误率适度膨胀，但显著提升了项目统计功效。