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.

中文翻译：

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使用估计框架处理非劣效性试验中的并发事件和缺失数据：结核病案例研究。

简介 ICH E9 附录概述了临床试验的估计框架，该附录于 2019 年发布，但对如何处理非劣效性研究的并发事件提供了有限的指导。一旦定义了估计值，也不清楚如何使用非劣效性研究的原则分析来处理缺失值。方法 以结核病临床试验作为案例研究，我们提出了一个主要估计值，以及一个适合非劣效性研究的附加估计值。为了进行估计，提出了与主要分析和敏感性分析的估计值一致的多重插补方法。我们演示了使用双重完全条件规范多重插补算法的估计方法，然后扩展并使用基于参考的多重插补二元结果以针对相关估计值，并提出每个估计值下的敏感性分析。我们将使用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，可以为非劣效性试验构建估计值，该试验改进了之前提倡的按方案/意向治疗类型分析人群，分别涉及假设或治疗政策策略来处理相关的并发情况事件。使用“双重”多重插补方法来估计主要假设估计值，并使用基于参考的方法来估计额外的治疗政策估计值（包括处理缺失数据的敏感性分析）的结果与原始研究报告的每个方案一致，并且意向治疗分析未能证明非劣效性。结论 使用精心构造的估计量和适当的主要估计量和敏感性估计量，使用所有可用的信息，可以产生更有原则性和统计上严格的分析方法。这样做可以提供对估计值的准确解释。