Power for Cluster-Randomized Trials: Software, Web app, and Methods

摘要 组群随机试验是一种越来越常见的试验设计，对某些人来说具有独特的优势 问题，其他人也需要。它还催生了几个相关的试验设计。最近备受瞩目的 使用这些设计的研究包括埃博拉疫苗和防止耐药性传播的试验 医院里的病原体。整群随机试验的关键特征是将受试者随机分组， 而不是作为个人，以便社区、医院或诊所的所有成员都得到相同的 治疗。 出于道德和伦理的原因，所有试验都必须有准确的威力和样本量计算。这样做是不对的 将超过良好权力所需的人数随机化，因为这会使多余的人面临 不必要的随机化。把更少的人随机化，而不是创造良好的权力，这也是错误的，就像所有人一样 毫无风险地暴露在研究中--几乎没有希望显示出任何治疗的好处--以及 缺乏研究效果可能是由于动力不足，而不是治疗无效。虽然它是一个次要的 考虑到这一点，样本量太小或太大也是不道德的，因为这浪费了稀缺的资源，如 调查人员的时间和资助者的美元。 对于一些整群随机试验设计，存在分析性(闭合)样本量公式 这些假设可能是不现实的。对于其他设计，只有近似公式存在。一般而言，这些 计算只能在教科书、科学论文和昂贵且困难的软件中找到 去理解和应用。对于最准确的计算，选择非常有限，这些计算基于 在模拟上。基于模拟的功率计算可以适应复杂的设计和逼真 只有在公式中才有可能出现的情景。 我们建议生成一个全面的免费和开源软件套件，以提供近似的、 分析型和基于模拟的功率评估。此外，我们将为代码开发一个Web应用程序，以允许 计算机知识较少的用户无法使用该软件。最后，我们将利用 用于回答整群随机试验设计中悬而未决问题的软件。

Summary Cluster-randomized trials are an increasingly common trial design that has unique advantages for some questions and is required for others. It has also spawned several related trial designs. Recent high-profile studies using these designs include trials of Ebola vaccines and for preventing the spread of resistant pathogens in hospitals. The key feature of cluster-randomized trials is that subjects are randomized in groups, rather than as individuals, so that all members of a community, hospital, or practice receive the same treatment. All trials must have accurate power and sample size calculation for moral and ethical reasons. It is wrong to randomize more persons than are needed for good power, as this exposes the excess persons to the risks of randomization unnecessarily. It is also wrong to randomize fewer persons than create good power, as then all are exposed to the risks of the study for naught—there is little hope of showing a benefit of any treatment-- and a lack of study effect may be due to poor power rather than ineffective treatments. Though it is a lesser concern, it is also unethical to have too small or large a sample size, as this wastes scarce resources such as the investigators’ time and the funder’s dollars. For some cluster-randomized trial designs, there exist analytic (closed-from) sample size formulae that rely on assumptions that can be unrealistic. For other designs, only approximate formulae exist. In general, these calculations can be found only in textbooks, scientific papers, and in software that is costly and can be difficult to understand and apply. There are very limited options for the most accurate calculations, which are based on simulations. Simulation-based power calculations can accommodate complex designs and realistic scenarios that are only awkwardly possible in formulae. We propose to generate a comprehensive free and open-source software suite to provide approximate, analytic, and simulation-based power assessment. In addition, we will develop a web app for the code to allow users who have less computing knowledge to make use of the software. Finally, we will make use of the software to answer outstanding questions in the design of cluster-randomized trials.