Eliciting and incorporating patients' opinions about missing data in randomised controlled trials

当数据无法分析时，就会出现数据丢失，这是临床试验中的一个常见挑战，可能会对结果的有效性产生严重后果。缺失数据试验的分析通常假设缺失数据是“随机缺失的”，即给定个体过去的观察数据，他们的退出概率不取决于他们现在(或未来)未观察到的结果。在许多情况下，这一假设是不可信的。因此，开发方法来评估结论对随机假设的偏离的稳健性是至关重要的。由于我们不能将假设建立在数据的基础上，一个有吸引力的方法是在试验的敏感性分析中纳入专家对缺失数据的原因和分布的意见。在过去，专家被定义为临床医生，并已开发出征求他们意见的方法。在这个过程中，患者被忽视了，尽管他们可能对患者丢失的数据有重要的意见要分享。目前，在试验分析的这一重要方面还没有可用的方法来征求患者的意见。目前的学生身份旨在开发和测试一种实用的、可访问的方法，允许有意义和准确地得出患者对临床试验中缺失数据的意见，并将其纳入试验的敏感性分析。该项目包括：1.对现有专家启发方法的文献进行回顾。基于1的发现，与患者小组共同设计一个工具，以得出他们对缺失数据的看法。这将包括由学生领导的一系列研讨会，以纳入小组对新工具应该是什么样子的观点，确定关键方面的优先顺序，以确保可行性并对其进行改进。评估在2中开发的工具，通过在一组患者中实施它，使用真实世界的试验作为例子，并基于Johnson等人概述的标准。关于贝叶斯启发式工具(参考：https://bit.ly/30j3iHV)，包括有效性、可靠性、响应性、可行性4。使用预先确定的方法，将在应用(3)中的工具时获得的意见纳入试验的敏感性分析，以评估结果的稳健性5。就临床试验中丢失的数据提出建议，以征求患者的意见

Missing data occurs when data is unavailable to be analysed and is a common challenge within clinical trials that can have serious consequences for the validity of results. The analysis of trials with missing data usually assumes the missing data are "missing at random", i.e. given an individual's past observed data, their probability of dropout does not depend on their present (or future) unobserved outcome.In many settings this assumption is implausible. For this reason, it is crucial to develop methods to assess the robustness of conclusions to departures from the missing at random assumption. Since we cannot base assumptions on data, an attractive approach is to incorporate experts' opinions about reasons for and distributions of the missing data in the trial's sensitivity analysis. In the past, experts have been defined as clinicians and methods to elicit their views have been developed. Patients have been overlooked in this process, even though they are likely to have important opinions to share regarding patient missing data. Currently, there is no method available to elicit patient's views in this important aspect of trial analysis.The current studentship aims to develop and test a practical, accessible approach that allows patient's opinions about missing data in a clinical trial to be meaningfully and accurately elicited and incorporated into a trial's sensitivity analyses.The project involves:1. Review of the literature on current expert elicitation methods available2. Based on the findings from 1, co-design with a patient panel a tool to elicit their views on missing data. This would include a series of workshops led by the student to incorporate the panel's views on what the new tool should look like, prioritise the key aspects to ensure feasibility and refine it.3. Evaluate the tool developed in 2, by implementing it with a group of patients, using a real-world trial as an example, and based on criteria outlined by Johnson et al. for Bayesian elicitation tools (reference: https://bit.ly/30j3iHV) including validity, reliability, responsiveness, feasibility4. Incorporate the opinions elicited in the application of the tool in (3) in a trial's sensitivity analysis, using pre-established methods, to assess the robustness of the findings5. Produce recommendations for the elicitation of patient's views regarding missing data in clinical trials