A common concern in non-inferiority (NI) trials is that non adherence due, for example, to poor study conduct can make treatment arms artificially similar. Because intention to treat analyses can be anti-conservative in this situation, per protocol analyses are sometimes recommended. However, such advice does not consider the estimands framework, nor the risk of bias from per protocol analyses. We therefore sought to update the above guidance using the estimands framework, and compare estimators to improve on the performance of per protocol analyses. We argue the main threat to validity of NI trials is the occurrence of trial specific intercurrent events (IEs), that is, IEs which occur in a trial setting, but would not occur in practice. To guard against erroneous conclusions of non inferiority, we suggest an estimand using a hypothetical strategy for trial specific IEs should be employed, with handling of other non trial specific IEs chosen based on clinical considerations. We provide an overview of estimators that could be used to estimate a hypothetical estimand, including inverse probability weighting (IPW), and two instrumental variable approaches (one using an informative Bayesian prior on the effect of standard treatment, and one using a treatment by covariate interaction as an instrument). We compare them, using simulation in the setting of all or nothing compliance in two active treatment arms, and conclude both IPW and the instrumental variable method using a Bayesian prior are potentially useful approaches, with the choice between them depending on which assumptions are most plausible for a given trial.

翻译：非劣效性（NI）试验中一个常见问题是，因研究实施不佳等原因导致的非依从性可能使治疗组人为趋同。由于意向性治疗分析在此情境下可能过于保守，有时会推荐采用符合方案分析。然而，此类建议未考虑估计量框架，也未涉及符合方案分析带来的偏倚风险。因此，我们试图利用估计量框架更新上述指南，并比较估计量以改进符合方案分析的性能。我们认为，NI试验有效性的主要威胁是试验特异性同期事件（IEs）的发生，即仅在试验环境中发生而在实际临床中不会出现的同期事件。为防止得出错误的非劣效性结论，我们建议采用假设性策略处理试验特异性同期事件，并基于临床考量选择其他非试验特异性同期事件的处理方法。本文概述了可用于估计假设估计量的方法，包括逆概率加权（IPW）及两种工具变量方法（一种基于标准治疗效果使用信息性贝叶斯先验，另一种利用治疗与协变量交互作用作为工具）。通过模拟两种活性治疗组全或无依从性的场景进行比较，结果表明IPW和使用贝叶斯先验的工具变量方法均为潜在有效方法，具体选择取决于给定试验中最合理的假设条件。