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

整群随机试验的力量：软件、Web 应用程序和方法

基本信息

批准号：
9897605
负责人：
Kenneth P. Kleinman
金额：
$ 27.94万
依托单位：
UNIVERSITY OF MASSACHUSETTS AMHERST
依托单位国家：
美国
项目类别：
财政年份：
2017
资助国家：
美国
起止时间：
2017-04-15 至 2023-03-31
项目状态：
已结题

项目摘要

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.

总结随机对照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，并要求其他人。它还催生了几个相关的试验设计。最近高调使用这些设计的研究包括埃博拉疫苗的试验和预防耐药病毒的传播。医院里的病原体整群随机试验的主要特点是受试者被随机分组，而不是作为个人，以便社区，医院或实践的所有成员都能获得同样的服务。治疗出于道德和伦理原因，所有试验都必须有准确的把握度和样本量计算。是错误随机化的人数超过了良好权力所需的人数，因为这会使多余的人面临以下风险：不必要的随机化。这也是错误的随机少的人比创造良好的权力，因为然后所有暴露在研究的风险中毫无意义-几乎没有希望显示任何治疗的益处-并且缺乏研究效果可能是由于把握度差，而不是治疗无效。虽然这是一个较小的关注，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，调查人员的时间和出资人的钱对于某些簇随机试验设计，存在依赖于以下内容的分析（封闭）样本量公式假设可能是不现实的。对于其他设计，仅存在近似公式。总的来说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理解和应用。最准确的计算的选择非常有限，这些计算基于在模拟上。基于仿真的功率计算可以适应复杂的设计，只有在公式中才有可能的情况。我们建议生成一个全面的免费和开源软件套件，分析和基于模拟的功率评估。此外，我们将开发一个Web应用程序的代码，以允许让电脑知识较少的用户使用该软件。最后，我们将利用软件，以回答突出的问题，在设计的集群随机试验。