Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
Yale Center for Analytical Sciences, Yale School of Public Health, New Haven, CT, USA.
Clin Trials. 2022 Feb;19(1):3-13. doi: 10.1177/17407745211051288. Epub 2021 Oct 24.
BACKGROUND/AIMS: When participants in individually randomized group treatment trials are treated by multiple clinicians or in multiple group treatment sessions throughout the trial, this induces partially nested clusters which can affect the power of a trial. We investigate this issue in the Whole Health Options and Pain Education trial, a three-arm pragmatic, individually randomized clinical trial. We evaluate whether partial clusters due to multiple visits delivered by different clinicians in the Whole Health Team arm and dynamic participant groups due to changing group leaders and/or participants across treatment sessions during treatment delivery in the Primary Care Group Education arm may impact the power of the trial. We also present a Bayesian approach to estimate the intraclass correlation coefficients.
We present statistical models for each treatment arm of Whole Health Options and Pain Education trial in which power is estimated under different intraclass correlation coefficients and mapping matrices between participants and clinicians or treatment sessions. Power calculations are based on pairwise comparisons. In practice, sample size calculations depend on estimates of the intraclass correlation coefficients at the treatment sessions and clinician levels. To accommodate such complexities, we present a Bayesian framework for the estimation of intraclass correlation coefficients under different participant-to-session and participant-to-clinician mapping scenarios. We simulated continuous outcome data based on various clinical scenarios in Whole Health Options and Pain Education trial using a range of intraclass correlation coefficients and mapping matrices and used Gibbs samplers with conjugate priors to obtain posteriors of the intraclass correlation coefficients under those different scenarios. Posterior means and medians and their biases are calculated for the intraclass correlation coefficients to evaluate the operating characteristics of the Bayesian intraclass correlation coefficient estimators.
Power for Whole Health Team versus Primary Care Group Education is sensitive to the intraclass correlation coefficient in the Whole Health Team arm. In these two arms, an increased number of clinicians, more evenly distributed workload of clinicians, or more homogeneous treatment group sizes leads to increased power. Our simulation study for the intraclass correlation coefficient estimation indicates that the posterior mean intraclass correlation coefficient estimator has less bias when the true intraclass correlation coefficients are large (i.e. 0.10), but when the intraclass correlation coefficient is small (i.e. 0.01), the posterior median intraclass correlation coefficient estimator is less biased.
Knowledge of intraclass correlation coefficients and the structure of clustering are critical to the design of individually randomized group treatment trials with partially nested clusters. We demonstrate that the intraclass correlation coefficient of the Whole Health Team arm can affect power in the Whole Health Options and Pain Education trial. A Bayesian approach provides a flexible procedure for estimating the intraclass correlation coefficients under complex scenarios. More work is needed to educate the research community about the individually randomized group treatment design and encourage publication of intraclass correlation coefficients to help inform future trial designs.
背景/目的：当个体随机分组治疗试验中的参与者在试验过程中由多个临床医生治疗或在多个分组治疗会议中接受治疗时，这会导致部分嵌套群集，从而影响试验的功效。我们在 Whole Health Options and Pain Education 试验中研究了这个问题，这是一项三臂实用的个体随机临床试验。我们评估了由于 Whole Health Team 臂中的多次就诊由不同临床医生进行以及由于在 Primary Care Group Education 臂中治疗期间治疗会议中治疗组组长和/或参与者的变化而导致的动态参与者组而导致的部分群集是否会影响试验的功效。我们还提出了一种贝叶斯方法来估计组内相关系数。
我们为 Whole Health Options and Pain Education 试验的每个治疗臂呈现了统计模型，其中在不同的组内相关系数和参与者与临床医生或治疗会议之间的映射矩阵下估计功效。功效计算基于两两比较。在实践中，样本量计算取决于治疗会议和临床医生水平的组内相关系数估计值。为了适应这种复杂性，我们提出了一种贝叶斯框架，用于在不同的参与者到会议和参与者到临床医生映射场景下估计组内相关系数。我们根据 Whole Health Options and Pain Education 试验中的各种临床情况使用一系列组内相关系数和映射矩阵模拟连续结果数据，并使用具有共轭先验的 Gibbs 抽样器获得那些不同场景下的组内相关系数的后验。计算组内相关系数的后验均值和中位数及其偏差，以评估贝叶斯组内相关系数估计器的工作特性。
Whole Health Team 与 Primary Care Group Education 之间的功效对 Whole Health Team 臂中的组内相关系数敏感。在这两个臂中，增加临床医生的数量、更均匀地分配临床医生的工作量或更均匀的治疗组大小会提高功效。我们对组内相关系数估计的模拟研究表明，当真实组内相关系数较大（即 0.10）时，后验均值组内相关系数估计器的偏差较小，但当组内相关系数较小时（即 0.01），后验中位数组内相关系数估计器的偏差较小。
对具有部分嵌套群集的个体随机分组治疗试验的组内相关系数和聚类结构的了解至关重要。我们证明了 Whole Health Team 臂的组内相关系数会影响 Whole Health Options and Pain Education 试验的功效。贝叶斯方法为在复杂情况下估计组内相关系数提供了灵活的程序。需要做更多的工作来教育研究界关于个体随机分组治疗设计的知识，并鼓励发表组内相关系数，以帮助为未来的试验设计提供信息。
