Department of Biostatistics, Collaborative Studies Coordinating Center.
Department of Epidemiology, Gillings School of Global Public Health.
Med Care. 2021 Aug 1;59(Suppl 4):S355-S363. doi: 10.1097/MLR.0000000000001580.
The COMprehensive Post-Acute Stroke Services study was a cluster-randomized pragmatic trial designed to evaluate a comprehensive care transitions model versus usual care. The data collected during this trial were complex and analysis methodology was required that could simultaneously account for the cluster-randomized design, missing patient-level covariates, outcome nonresponse, and substantial nonadherence to the intervention.
The objective of this study was to discuss an array of complementary statistical methods to evaluate treatment effectiveness that appropriately addressed the challenges presented by the complex data arising from this pragmatic trial.
We utilized multiple imputation combined with inverse probability weighting to account for missing covariate and outcome data in the estimation of intention-to-treat effects (ITT). The ITT estimand reflects the effectiveness of assignment to the COMprehensive Post-Acute Stroke Services intervention compared with usual care (ie, it does not take into account intervention adherence). Per-protocol analyses provide complementary information about the effect of treatment, and therefore are relevant for patients to inform their decision-making. We describe estimation of the complier average causal effect using an instrumental variables approach through 2-stage least squares estimation. For all preplanned analyses, we also discuss additional sensitivity analyses.
Pragmatic trials are well suited to inform clinical practice. Care should be taken to proactively identify the appropriate balance between control and pragmatism in trial design. Valid estimation of ITT and per-protocol effects in the presence of complex data requires application of appropriate statistical methods and concerted efforts to ensure high-quality data are collected.
COMprehensive Post-Acute Stroke Services 研究是一项集群随机实用试验，旨在评估综合护理过渡期模型与常规护理。在这项试验中收集的数据较为复杂，需要采用相应的分析方法，同时考虑集群随机设计、缺失的患者水平协变量、结果无应答和对干预措施的大量不依从性。
本研究旨在讨论一系列补充性统计方法，以评估治疗效果，这些方法适当处理了来自该实用试验的复杂数据所带来的挑战。
我们采用多重插补结合逆概率加权，以处理意向治疗（ITT）估计中缺失的协变量和结果数据。ITT 估计反映了与常规护理相比，COMprehensive Post-Acute Stroke Services 干预的分配效果（即，它不考虑干预措施的依从性）。方案分析提供了关于治疗效果的补充信息，因此对于患者来说，这些信息有助于他们做出决策。我们描述了通过两阶段最小二乘法估计，使用工具变量方法估计遵从平均因果效应。对于所有预先计划的分析，我们还讨论了其他敏感性分析。
实用试验非常适合为临床实践提供信息。在试验设计中，应注意主动确定控制和实用之间的适当平衡。在复杂数据的情况下，有效估计 ITT 和方案分析效果需要应用适当的统计方法，并共同努力确保收集高质量的数据。
