Missing data are an inevitable challenge in Randomised Controlled Trials (RCTs), particularly those with Patient Reported Outcome Measures. Methodological guidance suggests that to avoid incorrect conclusions, studies should undertake sensitivity analyses which recognise that data may be ‘missing not at random’ (MNAR). A recommended approach is to elicit expert opinion about the likely outcome differences for those with missing versus observed data. However, few published trials plan and undertake these elicitation exercises, and so lack the external information required for these sensitivity analyses. The aim of this paper is to provide a framework that anticipates and allows for MNAR data in the design and analysis of clinical trials. We developed a framework for performing and using expert elicitation to frame sensitivity analysis in RCTs with missing outcome data. The framework includes the following steps: first defining the scope of the elicitation exercise, second developing the elicitation tool, third eliciting expert opinion about the missing outcomes, fourth evaluating the elicitation results, and fifth analysing the trial data. We provide guidance on key practical challenges that arise when adopting this approach in trials: the criteria for identifying relevant experts, the outcome scale for presenting data to experts, the appropriate representation of expert opinion, and the evaluation of the elicitation results.The framework was developed within the POPPI trial, which investigated whether a preventive, complex psychological intervention, commenced early in ICU, would reduce the development of patient-reported post-traumatic stress disorder symptom severity, and improve health-related quality of life. We illustrate the key aspects of the proposed framework using the POPPI trial. For the POPPI trial, 113 experts were identified with potentially suitable knowledge and asked to participate in the elicitation exercise. The 113 experts provided 59 usable elicitation questionnaires. The sensitivity analysis found that the results from the primary analysis were robust to alternative MNAR mechanisms. Future studies can adopt this framework to embed expert elicitation within the design of clinical trials. This will provide the information required for MNAR sensitivity analyses that examine the robustness of the trial conclusions to alternative, but realistic assumptions about the missing data.

中文翻译：

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用于扩展试验设计以促进缺失数据敏感性分析的框架。


数据缺失是随机对照试验 (RCT) 中不可避免的挑战，特别是那些采用患者报告结果指标的试验。方法学指导建议，为了避免错误的结论，研究应进行敏感性分析，认识到数据可能“非随机缺失”（MNAR）。推荐的方法是征求专家关于缺失数据与观察到数据的结果可能存在差异的意见。然而，很少有已发表的试验计划并进行这些启发练习，因此缺乏这些敏感性分析所需的外部信息。本文的目的是提供一个框架，在临床试验的设计和分析中预测并允许使用 MNAR 数据。我们开发了一个框架，用于执行和使用专家启发来构建缺少结果数据的随机对照试验的敏感性分析。该框架包括以下步骤：首先定义启发练习的范围，其次开发启发工具，第三引出专家对缺失结果的意见，第四评估启发结果，第五分析试验数据。我们针对在试验中采用这种方法时出现的关键实际挑战提供指导：确定相关专家的标准、向专家提供数据的结果量表、专家意见的适当表述以及启发结果的评估。该框架是该研究是在 POPPI 试验中开发的，该试验调查了在 ICU 早期开始的预防性复杂心理干预是否会减少患者报告的创伤后应激障碍症状严重程度的发展，并改善与健康相关的生活质量。 我们使用 POPPI 试验说明了所提出框架的关键方面。对于 POPPI 试验，确定了 113 名具有潜在合适知识的专家，并要求他们参与启发活动。 113 名专家提供了 59 份可用的启发问卷。敏感性分析发现，初步分析的结果对于替代 MNAR 机制是稳健的。未来的研究可以采用这个框架，将专家启发嵌入到临床试验的设计中。这将为 MNAR 敏感性分析提供所需的信息，以检查试验结论对关于缺失数据的替代但现实的假设的稳健性。