Nugent Joshua R, Kakande Elijah, Chamie Gabriel, Kabami Jane, Owaraganise Asiphas, Havlir Diane V, Kamya Moses, Balzer Laura B
Division of Research, Kaiser Permanente Northern California, Pleasanton, CA, USA.
Infectious Diseases Research Collaboration, Kampala, Uganda.
Clin Trials. 2025 May 2:17407745251333779. doi: 10.1177/17407745251333779.
Participant dependence, if present, must be accounted for in the analysis of randomized trials. This dependence, also referred to as "clustering," can occur in one or more trial arms. This dependence may predate randomization or arise after randomization. We examine three trial designs: one "fully clustered" (where all participants are dependent within clusters or groups) and two "partially clustered" (where some participants are dependent within clusters and some participants are completely independent of all others).
For these three designs, we (1) use causal models to non-parametrically describe the data generating process and formalize the dependence in the observed data distribution; (2) develop a novel implementation of targeted minimum loss-based estimation for analysis; (3) evaluate the finite-sample performance of targeted minimum loss-based estimation and common alternatives via a simulation study; and (4) apply the methods to real-data from the SEARCH-IPT trial.
We show that the two randomization schemes resulting in partially clustered trials have the same dependence structure, enabling use of the same statistical methods for estimation and inference of causal effects. Our novel targeted minimum loss-based estimation approach leverages covariate adjustment and machine learning to improve precision and facilitates estimation of a large set of causal effects. In simulations, we demonstrate that targeted minimum loss-based estimation achieves comparable or markedly higher statistical power than common alternatives for these partially clustered designs. Finally, application of targeted minimum loss-based estimation to real data from the SEARCH-IPT trial resulted in 20%-57% efficiency gains, demonstrating the real-world consequences of our proposed approach.ConclusionsPartially clustered trial analysis can be made more efficient by implementing targeted minimum loss-based estimation, assuming care is taken to account for the dependent nature of the observed data.
如果存在参与者依赖性，在随机试验分析中必须予以考虑。这种依赖性，也称为“聚类”，可能出现在一个或多个试验组中。这种依赖性可能在随机分组之前就已存在，或者在随机分组之后出现。我们研究了三种试验设计：一种“完全聚类”（所有参与者在聚类或组内具有依赖性）和两种“部分聚类”（一些参与者在聚类内具有依赖性，而一些参与者与所有其他参与者完全独立）。
对于这三种设计，我们（1）使用因果模型以非参数方式描述数据生成过程，并将观察到的数据分布中的依赖性形式化；（2）开发一种基于目标最小损失估计的新颖实现方法用于分析；（3）通过模拟研究评估基于目标最小损失估计和常见替代方法的有限样本性能；（4）将这些方法应用于SEARCH-IPT试验的真实数据。
我们表明，导致部分聚类试验的两种随机化方案具有相同的依赖性结构，这使得能够使用相同的统计方法来估计和推断因果效应。我们新颖的基于目标最小损失估计的方法利用协变量调整和机器学习来提高精度，并便于估计大量因果效应。在模拟中，我们证明对于这些部分聚类设计，基于目标最小损失估计比常见替代方法具有相当或明显更高的统计功效。最后，将基于目标最小损失估计应用于SEARCH-IPT试验的真实数据，效率提高了20% - 57%，证明了我们提出的方法在现实世界中的影响。
假设注意到观察数据的依赖性本质，通过实施基于目标最小损失估计，可以提高部分聚类试验分析的效率。
