GlaxoSmithKline, Middlesex, UK.
Imperial Clinical Trials Unit, School of Public Health, Imperial College London, London, UK.
Clin Trials. 2023 Oct;20(5):497-506. doi: 10.1177/17407745231176773. Epub 2023 Jun 5.
INTRODUCTION: The ICH E9 addendum outlining the estimand framework for clinical trials was published in 2019 but provides limited guidance around how to handle intercurrent events for non-inferiority studies. Once an estimand is defined, it is also unclear how to deal with missing values using principled analyses for non-inferiority studies. METHODS: Using a tuberculosis clinical trial as a case study, we propose a primary estimand, and an additional estimand suitable for non-inferiority studies. For estimation, multiple imputation methods that align with the estimands for both primary and sensitivity analysis are proposed. We demonstrate estimation methods using the twofold fully conditional specification multiple imputation algorithm and then extend and use reference-based multiple imputation for a binary outcome to target the relevant estimands, proposing sensitivity analyses under each. We compare the results from using these multiple imputation methods with those from the original study. RESULTS: Consistent with the ICH E9 addendum, estimands can be constructed for a non-inferiority trial which improves on the per-protocol/intention-to-treat-type analysis population previously advocated, involving respectively a hypothetical or treatment policy strategy to handle relevant intercurrent events. Results from using the 'twofold' multiple imputation approach to estimate the primary hypothetical estimand, and using reference-based methods for an additional treatment policy estimand, including sensitivity analyses to handle the missing data, were consistent with the original study's reported per-protocol and intention-to-treat analysis in failing to demonstrate non-inferiority. CONCLUSIONS: Using carefully constructed estimands and appropriate primary and sensitivity estimators, using all the information available, results in a more principled and statistically rigorous approach to analysis. Doing so provides an accurate interpretation of the estimand.
简介:ICH E9 附录概述了临床试验的估计量框架,该附录于 2019 年发布,但对非劣效性研究中如何处理并发事件提供的指导有限。一旦定义了估计量,对于非劣效性研究,如何使用有原则的分析方法处理缺失值也不清楚。
方法:使用结核病临床试验作为案例研究,我们提出了一个主要的估计量和一个适合非劣效性研究的额外估计量。对于估计,我们提出了与主要和敏感性分析的估计量一致的多种插补方法。我们使用两倍完全条件指定多重插补算法演示了估计方法,然后扩展并使用基于参考的多重插补方法来处理二分类结局,针对每个估计量提出敏感性分析。我们将这些多重插补方法的结果与原始研究的结果进行比较。
结果:与 ICH E9 附录一致,可以为非劣效试验构建估计量,这比以前提倡的方案/意向治疗型分析人群有所改进,涉及到处理相关并发事件的假设或治疗策略。使用“两倍”多重插补方法估计主要假设估计量,以及使用基于参考的方法估计额外的治疗策略估计量,包括处理缺失数据的敏感性分析,结果与原始研究报告的方案和意向治疗分析不一致,未能证明非劣效性。
结论:使用精心构建的估计量和适当的主要和敏感性估计量,利用所有可用信息,可采用更有原则和更严格的统计方法进行分析。这样做可以对估计量进行准确的解释。
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