Ontario Child Health Support Unit, SickKids Research Institute, Toronto, ON, Canada.
Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.
Clin Trials. 2020 Aug;17(4):414-419. doi: 10.1177/1740774520914306. Epub 2020 Apr 16.
BACKGROUND/AIMS: The use of pilot studies to help inform the design of randomized controlled trials has increased significantly over the last couple of decades. A pilot study can provide estimates of feasibility parameters, such as the recruitment, compliance and follow-up probabilities. The use of frequentist confidence intervals of these estimates fails to provide a meaningful measure of the uncertainty as it pertains to the design of the associated randomized controlled trial. The objective of this article is to introduce Bayesian methods for the analysis of pilot studies for determining the feasibility of an associated randomized controlled trial.
An example from the literature is used to illustrate the advantages of a Bayesian approach for accounting for the uncertainty in pilot study results when assessing the feasibility of an associated randomized controlled trial. Vague beta distribution priors for the feasibility parameters are used. Based on the results from a feasibility study, simulation methods are used to determine the expected power of specified recruitment strategies for an associated randomized controlled trial.
The vague priors used for the feasibility parameters are demonstrated to be considerably robust. Beta distribution posteriors for the feasibility parameters lead to beta-binomial predictive distributions for an associated randomized controlled trial regarding the number of patients randomized, the number of patients who are compliant and the number of patients who complete follow-up. Ignoring the uncertainty in pilot study results can lead to inadequate power for an associated randomized controlled trial.
Applying Bayesian methods to pilot studies' results provides direct inference about the feasibility parameters and quantifies the uncertainty regarding the feasibility of an associated randomized controlled trial in an intuitive and meaningful way. Furthermore, Bayesian methods can identify recruitment strategies that yield the desired power for an associated randomized controlled trial.
背景/目的：在过去的几十年中，使用初步研究来帮助确定随机对照试验的设计已经大大增加。初步研究可以提供可行性参数的估计，例如招募、依从性和随访概率。这些估计的频率置信区间并不能为与相关随机对照试验设计相关的不确定性提供有意义的衡量标准。本文的目的是介绍用于分析初步研究以确定相关随机对照试验可行性的贝叶斯方法。
本文使用文献中的一个例子来说明贝叶斯方法在评估相关随机对照试验可行性时，对初步研究结果不确定性进行分析的优势。对可行性参数使用模糊 beta 分布先验。基于可行性研究的结果，使用模拟方法确定指定招募策略对相关随机对照试验的预期功效。
用于可行性参数的模糊先验被证明具有相当的稳健性。可行性参数的 beta 分布后验导致与相关随机对照试验关于随机化患者数量、依从患者数量和完成随访患者数量的 beta-binomial 预测分布。忽略初步研究结果的不确定性可能导致相关随机对照试验的功效不足。
将贝叶斯方法应用于初步研究结果提供了关于可行性参数的直接推断，并以直观和有意义的方式量化了与相关随机对照试验可行性相关的不确定性。此外，贝叶斯方法可以确定产生相关随机对照试验所需功效的招募策略。
