Clinical Trials Research Unit, Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, UK.
Institute of Health and Society, Newcastle University, Newcastle upon Tyne, UK.
Stat Med. 2021 May 30;40(12):2877-2892. doi: 10.1002/sim.8941. Epub 2021 Mar 17.
External pilot trials of complex interventions are used to help determine if and how a confirmatory trial should be undertaken, providing estimates of parameters such as recruitment, retention, and adherence rates. The decision to progress to the confirmatory trial is typically made by comparing these estimates to pre-specified thresholds known as progression criteria, although the statistical properties of such decision rules are rarely assessed. Such assessment is complicated by several methodological challenges, including the simultaneous evaluation of multiple endpoints, complex multi-level models, small sample sizes, and uncertainty in nuisance parameters. In response to these challenges, we describe a Bayesian approach to the design and analysis of external pilot trials. We show how progression decisions can be made by minimizing the expected value of a loss function, defined over the whole parameter space to allow for preferences and trade-offs between multiple parameters to be articulated and used in the decision-making process. The assessment of preferences is kept feasible by using a piecewise constant parametrization of the loss function, the parameters of which are chosen at the design stage to lead to desirable operating characteristics. We describe a flexible, yet computationally intensive, nested Monte Carlo algorithm for estimating operating characteristics. The method is used to revisit the design of an external pilot trial of a complex intervention designed to increase the physical activity of care home residents.
外部先导试验常用于复杂干预措施，以帮助确定是否以及如何进行确证性试验，提供招募、保留和依从率等参数的估计值。进展到确证性试验的决定通常是通过将这些估计值与称为进展标准的预先指定阈值进行比较来做出的，尽管这些决策规则的统计性质很少得到评估。这种评估受到多个方法学挑战的影响，包括同时评估多个终点、复杂的多层次模型、样本量小以及混杂参数的不确定性。为了应对这些挑战，我们描述了一种用于设计和分析外部先导试验的贝叶斯方法。我们展示了如何通过最小化损失函数的期望值来做出进展决策，该损失函数定义在整个参数空间上，以允许在多个参数之间表达和使用偏好和权衡，从而进行决策。通过使用损失函数的分段常数参数化来保持偏好评估的可行性，该参数在设计阶段选择，以导致理想的操作特性。我们描述了一种灵活但计算密集的嵌套蒙特卡罗算法，用于估计操作特性。该方法用于重新设计旨在增加养老院居民身体活动的复杂干预措施的外部先导试验。
