Global Product Development, Pfizer Inc, Groton, CT, 06340, USA.
Department of Statistics, Iowa State University, Ames, IA, 50011, USA.
BMC Med Res Methodol. 2022 Mar 27;22(1):82. doi: 10.1186/s12874-022-01509-9.
In the past few decades various methods have been proposed to handle missing data of clinical studies, so as to assess the robustness of primary results. Some of the methods are based on the assumption of missing at random (MAR) which assumes subjects who discontinue the treatment will maintain the treatment effect after discontinuation. The agency, however, has expressed concern over methods based on this overly optimistic assumption, because it hardly holds for subjects discontinuing the investigational drug. Although in recent years a good number of sensitivity analyses based on missing not at random (MNAR) assumptions have been proposed, some use very conservative assumption on which it might be hard for sponsors and regulators to reach common ground.
Here we propose a multiple imputation method targeting at "treatment policy" estimand based on the MNAR assumption. This method can be used as the primary analysis, in addition to serving as a sensitivity analysis. It imputes missing data using information from retrieved dropouts defined as subjects who remain in the study despite occurrence of intercurrent events. Then imputed data long with completers and retrieved dropouts are analyzed altogether and finally multiple results are summarized into a single estimate. According to definition in ICH E9 (R1), this proposed approach fully aligns with the treatment policy estimand but its assumption is much more realistic and reasonable.
Our approach has well controlled type I error rate with no loss of power. As expected, the effect size estimates take into account any dilution effect contributed by retrieved dropouts, conforming to the MNAR assumption.
Although multiple imputation approaches are always used as sensitivity analyses, this multiple imputation approach can be used as primary analysis for trials with sufficient retrieved dropouts or trials designed to collect retrieved dropouts.
在过去几十年中，已经提出了各种方法来处理临床研究中的缺失数据，以评估主要结果的稳健性。其中一些方法基于缺失数据随机（MAR）的假设，该假设假设停止治疗的受试者在停止治疗后仍将保持治疗效果。然而，该机构对基于这一过于乐观假设的方法表示关注，因为对于停止使用研究药物的受试者来说，这种假设几乎不可能成立。尽管近年来已经提出了许多基于缺失数据非随机（MNAR）假设的敏感性分析方法，但其中一些方法采用了非常保守的假设，这使得赞助商和监管机构难以达成共识。
在这里，我们提出了一种基于 MNAR 假设的针对“治疗方案”估计量的多重插补方法。除了作为敏感性分析之外，该方法还可以作为主要分析。它使用从检索到的脱落者中获取的信息来插补缺失数据，这些脱落者被定义为尽管发生了并发事件仍留在研究中的受试者。然后，将插补数据与完成者和检索到的脱落者一起进行分析，最后将多个结果总结为一个单一的估计值。根据 ICH E9（R1）的定义，这种方法完全符合治疗方案估计量，但它的假设更现实和合理。
我们的方法具有良好的控制 I 类错误率，不会损失功效。如预期的那样，效应大小估计值考虑了检索到的脱落者所带来的任何稀释效应，符合 MNAR 假设。
尽管多重插补方法通常用作敏感性分析，但对于有足够检索到的脱落者的试验或旨在收集检索到的脱落者的试验，这种多重插补方法可以用作主要分析。
