Applying the estimands framework to non-inferiority trials: guidance on choice of hypothetical estimands for non-adherence and comparison of estimation methods
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非劣效试验中的估计目标框架：不依从的假想目标与估计方法比较
- 研究问题
- 非劣效试验中的不依从可能使治疗组人为趋同，如何区分试验特有与实践中也会发生的中间事件，并定义合适的因果估计目标？
- 方法与思路
- 作者建议对试验特有事件采用假想策略目标，比较 IPW、带信息性贝叶斯先验的工具变量法，以及以治疗与协变量交互项作工具的方法。
- 材料陈述的贡献
- 摘要称，IPW 与使用贝叶斯先验的工具变量法具有潜在用途，两者选择取决于假设可信度；研究据估计目标框架更新符合方案分析建议。
- 阅读关注点
- 应核对试验特有事件的判定、假想目标定义与非劣效判据，以及加权和工具变量方法的识别条件、先验来源与模拟设置。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核验；模拟限于两个活性治疗臂的全或无依从，摘要未提供数值或更一般依从情形的结果。
原文摘要
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.