In some randomized (drug versus placebo) clinical trials, the estimand of interest is the between-treatment difference in population means of a clinical endpoint that is free from the confounding effects of "rescue" medication (e.g., HbA1c change from baseline at 24 weeks that would be observed without rescue medication regardless of whether or when the assigned treatment was discontinued). In such settings, a missing data problem arises if some patients prematurely discontinue from the trial or initiate rescue medication while in the trial, the latter necessitating the discarding of post-rescue data. We caution that the commonly used mixed-effects model repeated measures analysis with the embedded missing at random assumption can deliver an exaggerated estimate of the aforementioned estimand of interest. This happens, in part, due to implicit imputation of an overly optimistic mean for "dropouts" (i.e., patients with missing endpoint data of interest) in the drug arm. We propose an alternative approach in which the missing mean for the drug arm dropouts is explicitly replaced with either the estimated mean of the entire endpoint distribution under placebo (primary analysis) or a sequence of increasingly more conservative means within a tipping point framework (sensitivity analysis); patient-level imputation is not required. A supplemental "dropout = failure" analysis is considered in which a common poor outcome is imputed for all dropouts followed by a between-treatment comparison using quantile regression. All analyses address the same estimand and can adjust for baseline covariates. Three examples and simulation results are used to support our recommendations.

译文

:在一些随机的(药物与安慰剂)临床试验中,关注的估计是临床终点的人群均值之间的治疗差异,而不受“拯救”药物的混杂影响(例如,HbA1c在24岁时偏离基线)不论是否或何时终止指定的治疗,都无需救援药物即可观察到的数周时间)。在这种情况下,如果某些患者在试验期间过早中断试验或开始抢救药物,则会出现数据丢失的问题,后者需要丢弃救援后的数据。我们提醒您,通常使用的混合效应模型重复测量分析会在随机假设的情况下出现嵌入缺失,从而可能对上述感兴趣的估计值进行夸大的估计。之所以会发生这种情况,部分原因是由于对药物部门中的“辍学”(即患者缺少目标终点数据缺失的患者)过于乐观的平均值的隐式估算。我们提出了另一种方法,即用安慰剂(主要分析)下整个终点分布的估计均值(主要分析)或临界点框架内一系列越来越保守的均值(敏感性分析)明确替换掉药物臂退出的均值);不需要患者级别的估算。考虑进行补充的“辍学=失败”分析,在该分析中,对所有辍学的普遍不良结果进行了归因,然后使用分位数回归进行了治疗之间的比较。所有分析都针对相同的估计值,并且可以针对基线协变量进行调整。使用三个示例和仿真结果来支持我们的建议。

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