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

缺失数据发生在数据无法进行分析时，是临床试验中的常见挑战，可能对结果的有效性产生严重后果。缺失数据的试验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，其脱落概率不取决于其当前（或未来）未观察到的结果。在许多情况下，这种假设是不可信的。出于这个原因，它是至关重要的，以发展的方法来评估的稳健性的结论偏离随机缺失的假设。由于我们不能基于数据进行假设，因此一种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，并且已经开发了获取他们观点的方法。在这个过程中，患者被忽视了，尽管他们可能对患者缺失的数据有重要的意见。目前，在临床试验分析的这一重要方面，还没有方法可以获取患者的意见。本研究旨在开发和测试一种实用的、可访问的方法，使患者对临床试验中缺失数据的意见能够被有意义地、准确地获取，并纳入试验的敏感性分析中。审查现有的专家启发方法的文献2。根据1的发现，与患者小组共同设计一种工具，以了解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看法，优先考虑关键方面，以确保可行性并完善它。以真实世界试验为例，并基于约翰逊等人为贝叶斯启发工具（参考：https：//bit.ly/30j3iHV）概述的标准（包括有效性、可靠性、响应性和可行性4），通过在一组患者中实施来评价2中开发的工具。使用预先确定的方法，将（3）中工具应用中得出的意见纳入试验的敏感性分析，以评估结果的稳健性5。就临床试验中缺失的数据提出征求患者意见的建议

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