Missing data in clinical trials: control-based mean imputation and sensitivity analysis
Missing data in clinical trials: control-based mean imputation and sensitivity analysis
复制标题
DOI：
10.1002/pst.1817
复制
发表时间：
2017-09-01
影响因子：
1.5
通讯作者：
Permutt, Thomas
中科院分区：
文献类型：
--
作者：
Mehrotra, Devan V.;Liu, Fang;Permutt, Thomas
在一些随机（药物与安慰剂）临床试验中，关注的被估量是临床终点的群体平均值的治疗间差异，该临床终点不受补救药物的混杂效应（例如，在未使用补救药物的情况下观察到的第24周时HbA1c较基线的变化，无论是否或何时停止分配的治疗）。在这种情况下，如果一些患者提前退出试验或在试验期间开始使用补救药物，则会出现缺失数据问题，后者需要丢弃补救后数据。我们注意到，常用的混合效应模型重复测量分析与嵌入随机缺失假设可以提供夸大的估计上述被估量的利益。发生这种情况的部分原因是对辍学者过于乐观的平均值进行了隐式估算（即，我们提出了一种替代方法，其中药物组脱落的缺失平均值明确替换为安慰剂下整个终点分布的估计平均值（主要分析）或临界点框架内越来越保守的平均值序列（敏感性分析）;不需要患者水平插补。考虑补充脱落=失败分析，其中对所有脱落进行常见不良结局插补，然后使用分位数回归进行治疗间比较。所有分析均涉及相同的被估量，并可针对基线协变量进行调整。三个例子和仿真结果被用来支持我们的建议。
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 24weeks 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.