Mason Alexina J, Gomes Manuel, Grieve Richard, Ulug Pinar, Powell Janet T, Carpenter James
1 Department of Health Services Research and Policy, London School of Hygiene & Tropical Medicine, London, UK.
2 Vascular Surgery Research Group, Imperial College London, London, UK.
Clin Trials. 2017 Aug;14(4):357-367. doi: 10.1177/1740774517711442. Epub 2017 Jul 4.
BACKGROUND/AIMS: The analyses of randomised controlled trials with missing data typically assume that, after conditioning on the observed data, the probability of missing data does not depend on the patient's outcome, and so the data are 'missing at random' . This assumption is usually implausible, for example, because patients in relatively poor health may be more likely to drop out. Methodological guidelines recommend that trials require sensitivity analysis, which is best informed by elicited expert opinion, to assess whether conclusions are robust to alternative assumptions about the missing data. A major barrier to implementing these methods in practice is the lack of relevant practical tools for eliciting expert opinion. We develop a new practical tool for eliciting expert opinion and demonstrate its use for randomised controlled trials with missing data.
We develop and illustrate our approach for eliciting expert opinion with the IMPROVE trial (ISRCTN 48334791), an ongoing multi-centre randomised controlled trial which compares an emergency endovascular strategy versus open repair for patients with ruptured abdominal aortic aneurysm. In the IMPROVE trial at 3 months post-randomisation, 21% of surviving patients did not complete health-related quality of life questionnaires (assessed by EQ-5D-3L). We address this problem by developing a web-based tool that provides a practical approach for eliciting expert opinion about quality of life differences between patients with missing versus complete data. We show how this expert opinion can define informative priors within a fully Bayesian framework to perform sensitivity analyses that allow the missing data to depend upon unobserved patient characteristics.
A total of 26 experts, of 46 asked to participate, completed the elicitation exercise. The elicited quality of life scores were lower on average for the patients with missing versus complete data, but there was considerable uncertainty in these elicited values. The missing at random analysis found that patients randomised to the emergency endovascular strategy versus open repair had higher average (95% credible interval) quality of life scores of 0.062 (-0.005 to 0.130). Our sensitivity analysis that used the elicited expert information as pooled priors found that the gain in average quality of life for the emergency endovascular strategy versus open repair was 0.076 (-0.054 to 0.198).
We provide and exemplify a practical tool for eliciting the expert opinion required by recommended approaches to the sensitivity analyses of randomised controlled trials. We show how this approach allows the trial analysis to fully recognise the uncertainty that arises from making alternative, plausible assumptions about the reasons for missing data. This tool can be widely used in the design, analysis and interpretation of future trials, and to facilitate this, materials are available for download.
背景/目的:对存在缺失数据的随机对照试验进行分析时,通常假定在以观察到的数据为条件后,数据缺失的概率不取决于患者的结局,即数据“随机缺失”。例如,这一假设通常不太合理,因为健康状况相对较差的患者可能更易退出试验。方法学指南建议试验需进行敏感性分析,最好依据专家意见来进行,以评估结论对于关于缺失数据的替代假设是否稳健。在实际中实施这些方法的一个主要障碍是缺乏用于获取专家意见的相关实用工具。我们开发了一种用于获取专家意见的新实用工具,并展示了其在存在缺失数据的随机对照试验中的应用。
我们通过IMPROVE试验(ISRCTN 48334791)来开发并阐述我们获取专家意见的方法,IMPROVE试验是一项正在进行的多中心随机对照试验,比较了腹主动脉瘤破裂患者的急诊血管内治疗策略与开放修复术。在IMPROVE试验中,随机分组后3个月时,21%的存活患者未完成健康相关生活质量问卷(通过EQ-5D-3L评估)。我们通过开发一个基于网络工具来解决这一问题,该工具为获取关于缺失数据患者与完整数据患者生活质量差异的专家意见提供了一种实用方法。我们展示了这种专家意见如何在完全贝叶斯框架内定义信息性先验,以进行敏感性分析,使缺失数据能够取决于未观察到的患者特征。
在受邀参与的46位专家中,共有26位完成了意见获取工作。缺失数据患者的生活质量得分平均低于完整数据患者,但这些获取到的值存在相当大的不确定性。随机缺失分析发现,随机接受急诊血管内治疗策略与开放修复术的患者,其平均(95%可信区间)生活质量得分更高,为0.062(-0.005至0.130)。我们将获取到的专家信息用作合并先验的敏感性分析发现,急诊血管内治疗策略与开放修复术相比,平均生活质量的提升为0.076(-0.054至0.198)。
我们提供并举例说明了一种实用工具,用于获取随机对照试验敏感性分析推荐方法所需的专家意见。我们展示了这种方法如何使试验分析能够充分认识到因对数据缺失原因做出替代的、合理的假设而产生的不确定性。该工具可广泛应用于未来试验的设计、分析和解释,为便于使用,相关材料可供下载。