• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

针对存在健康结局缺失情况的随机对照试验,开发一种实用的专家意见征集方法:在IMPROVE试验中的应用。

Development of a practical approach to expert elicitation for randomised controlled trials with missing health outcomes: Application to the IMPROVE trial.

作者信息

Mason Alexina J, Gomes Manuel, Grieve Richard, Ulug Pinar, Powell Janet T, Carpenter James

机构信息

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.

出版信息

Clin Trials. 2017 Aug;14(4):357-367. doi: 10.1177/1740774517711442. Epub 2017 Jul 4.

DOI:10.1177/1740774517711442
PMID:28675302
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5648050/
Abstract

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.

METHODS

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.

RESULTS

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).

CONCLUSION

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.

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/798f/5648050/14510db4285c/10.1177_1740774517711442-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/798f/5648050/efe799c693d0/10.1177_1740774517711442-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/798f/5648050/912e99c705ac/10.1177_1740774517711442-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/798f/5648050/73c8c4c94d1c/10.1177_1740774517711442-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/798f/5648050/14510db4285c/10.1177_1740774517711442-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/798f/5648050/efe799c693d0/10.1177_1740774517711442-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/798f/5648050/912e99c705ac/10.1177_1740774517711442-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/798f/5648050/73c8c4c94d1c/10.1177_1740774517711442-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/798f/5648050/14510db4285c/10.1177_1740774517711442-fig4.jpg
摘要

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。

方法

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。

结果

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)。

结论

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。

相似文献

1
Development of a practical approach to expert elicitation for randomised controlled trials with missing health outcomes: Application to the IMPROVE trial.针对存在健康结局缺失情况的随机对照试验,开发一种实用的专家意见征集方法:在IMPROVE试验中的应用。
Clin Trials. 2017 Aug;14(4):357-367. doi: 10.1177/1740774517711442. Epub 2017 Jul 4.
2
A framework for extending trial design to facilitate missing data sensitivity analyses.扩展试验设计以促进缺失数据敏感性分析的框架。
BMC Med Res Methodol. 2020 Mar 17;20(1):66. doi: 10.1186/s12874-020-00930-2.
3
Eliciting and using expert opinions about dropout bias in randomized controlled trials.征集并运用关于随机对照试验中失访偏倚的专家意见。
Clin Trials. 2007;4(2):125-39. doi: 10.1177/1740774507077849.
4
Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas.在流行地区,服用抗叶酸抗疟药物的人群中,叶酸补充剂与疟疾易感性和严重程度的关系。
Cochrane Database Syst Rev. 2022 Feb 1;2(2022):CD014217. doi: 10.1002/14651858.CD014217.
5
A Bayesian framework for health economic evaluation in studies with missing data.基于贝叶斯框架的缺失数据下健康经济评价研究。
Health Econ. 2018 Nov;27(11):1670-1683. doi: 10.1002/hec.3793. Epub 2018 Jul 3.
6
Remote, real-time expert elicitation to determine the prior probability distribution for Bayesian sample size determination in international randomised controlled trials: Bronchiolitis in Infants Placebo Versus Epinephrine and Dexamethasone (BIPED) study.远程实时专家 elicitation 以确定贝叶斯样本量确定的先验概率分布在国际随机对照试验: 婴儿毛细支气管炎安慰剂与肾上腺素和地塞米松 (BIPED) 研究。
Trials. 2022 Apr 11;23(1):279. doi: 10.1186/s13063-022-06240-w.
7
A Bayesian framework to account for uncertainty due to missing binary outcome data in pairwise meta-analysis.一种用于在成对荟萃分析中处理因二元结局数据缺失而产生不确定性的贝叶斯框架。
Stat Med. 2015 May 30;34(12):2062-80. doi: 10.1002/sim.6475. Epub 2015 Mar 24.
8
The future of Cochrane Neonatal.考克兰新生儿协作网的未来。
Early Hum Dev. 2020 Nov;150:105191. doi: 10.1016/j.earlhumdev.2020.105191. Epub 2020 Sep 12.
9
A four-step strategy for handling missing outcome data in randomised trials affected by a pandemic.应对受大流行病影响的随机试验中缺失结局数据的四步策略。
BMC Med Res Methodol. 2020 Aug 12;20(1):208. doi: 10.1186/s12874-020-01089-6.
10
Multiple imputation to deal with missing EQ-5D-3L data: Should we impute individual domains or the actual index?采用多重填补法处理EQ-5D-3L数据缺失问题:我们应该填补各个维度还是实际指数?
Qual Life Res. 2015 Apr;24(4):805-15. doi: 10.1007/s11136-014-0837-y. Epub 2014 Dec 4.

引用本文的文献

1
Application of the estimand framework for an emulated trial using reference based multiple imputation to investigate informative censoring.应用估计量框架,通过基于参考的多重插补模拟试验,研究信息性删失。
BMC Med Res Methodol. 2024 Oct 18;24(1):245. doi: 10.1186/s12874-024-02364-6.
2
The UK resuscitative endovascular balloon occlusion of the aorta in trauma patients with life-threatening torso haemorrhage: the (UK-REBOA) multicentre RCT.英国创伤患者伴危及生命的躯干出血行主动脉腔内球囊阻断复苏治疗的多中心 RCT(UK-REBOA)研究。
Health Technol Assess. 2024 Sep;28(54):1-122. doi: 10.3310/LTYV4082.
3
Remote, real-time expert elicitation to determine the prior probability distribution for Bayesian sample size determination in international randomised controlled trials: Bronchiolitis in Infants Placebo Versus Epinephrine and Dexamethasone (BIPED) study.

本文引用的文献

1
Endovascular strategy or open repair for ruptured abdominal aortic aneurysm: one-year outcomes from the IMPROVE randomized trial.腹主动脉瘤破裂的血管内治疗策略或开放修复术:IMPROVE随机试验的一年结果
Eur Heart J. 2015 Aug 14;36(31):2061-2069. doi: 10.1093/eurheartj/ehv125. Epub 2015 Apr 7.
2
Handling missing data in RCTs; a review of the top medical journals.随机对照试验中缺失数据的处理;顶级医学期刊综述
BMC Med Res Methodol. 2014 Nov 19;14:118. doi: 10.1186/1471-2288-14-118.
3
A guide to handling missing data in cost-effectiveness analysis conducted within randomised controlled trials.
远程实时专家 elicitation 以确定贝叶斯样本量确定的先验概率分布在国际随机对照试验: 婴儿毛细支气管炎安慰剂与肾上腺素和地塞米松 (BIPED) 研究。
Trials. 2022 Apr 11;23(1):279. doi: 10.1186/s13063-022-06240-w.
4
Borrowing information across patient subgroups in clinical trials, with application to a paediatric trial.在临床试验中跨患者亚组借用信息,并应用于儿科试验。
BMC Med Res Methodol. 2022 Feb 20;22(1):49. doi: 10.1186/s12874-022-01539-3.
5
Sensitivity to missing not at random dropout in clinical trials: Use and interpretation of the trimmed means estimator.临床试验中对非随机缺失数据的敏感性:修剪均值估计量的使用和解释。
Stat Med. 2022 Apr 15;41(8):1462-1481. doi: 10.1002/sim.9299. Epub 2022 Jan 31.
6
What Do You Think? Using Expert Opinion to Improve Predictions of Response Propensity Under a Bayesian Framework.你怎么看?在贝叶斯框架下利用专家意见改进反应倾向预测。
Methoden Daten Anal. 2020;14(2). doi: 10.12758/mda.2020.05.
7
Reduced exposure to vasopressors through permissive hypotension to reduce mortality in critically ill people aged 65 and over: the 65 RCT.通过允许性低血压减少血管加压药的使用以降低 65 岁及以上危重症患者死亡率的 65 RCT 研究
Health Technol Assess. 2021 Feb;25(14):1-90. doi: 10.3310/hta25140.
8
Missing data: A statistical framework for practice.缺失数据:一种实践的统计框架。
Biom J. 2021 Jun;63(5):915-947. doi: 10.1002/bimj.202000196. Epub 2021 Feb 24.
9
Framework for the treatment and reporting of missing data in observational studies: The Treatment And Reporting of Missing data in Observational Studies framework.观察性研究中缺失数据的处理和报告框架:观察性研究中缺失数据的处理和报告框架。
J Clin Epidemiol. 2021 Jun;134:79-88. doi: 10.1016/j.jclinepi.2021.01.008. Epub 2021 Feb 2.
10
Integrating expert opinions with clinical trial data to analyse low-powered subgroup analyses: a Bayesian analysis of the VeRDiCT trial.将专家意见与临床试验数据相结合分析低效能亚组分析:VeRDiCT 试验的贝叶斯分析。
BMC Med Res Methodol. 2020 Dec 10;20(1):300. doi: 10.1186/s12874-020-01178-6.
随机对照试验中成本效益分析中缺失数据处理指南。
Pharmacoeconomics. 2014 Dec;32(12):1157-70. doi: 10.1007/s40273-014-0193-3.
4
Bayesian methods for the design and interpretation of clinical trials in very rare diseases.用于极罕见疾病临床试验设计与解读的贝叶斯方法。
Stat Med. 2014 Oct 30;33(24):4186-201. doi: 10.1002/sim.6225. Epub 2014 Jun 23.
5
Endovascular or open repair strategy for ruptured abdominal aortic aneurysm: 30 day outcomes from IMPROVE randomised trial.腹主动脉瘤破裂的腔内或开放修复策略:来自 IMPROVE 随机试验的 30 天结果。
BMJ. 2014 Jan 13;348:f7661. doi: 10.1136/bmj.f7661.
6
The prevention and treatment of missing data in clinical trials.临床试验中缺失数据的预防与处理
N Engl J Med. 2012 Oct 4;367(14):1355-60. doi: 10.1056/NEJMsr1203730.
7
Including all individuals is not enough: lessons for intention-to-treat analysis.纳入所有个体并不足够:意向治疗分析的教训。
Clin Trials. 2012 Aug;9(4):396-407. doi: 10.1177/1740774512450098. Epub 2012 Jul 2.
8
Strategy for intention to treat analysis in randomised trials with missing outcome data.随机试验中缺失结局数据的意向治疗分析策略。
BMJ. 2011 Feb 7;342:d40. doi: 10.1136/bmj.d40.
9
Missing outcomes in randomized trials: addressing the dilemma.随机试验中缺失的结果:解决困境。
Open Med. 2009 May 12;3(2):e51-3.
10
Methods to elicit beliefs for Bayesian priors: a systematic review.贝叶斯先验信念的 elicitation 方法:系统综述。
J Clin Epidemiol. 2010 Apr;63(4):355-69. doi: 10.1016/j.jclinepi.2009.06.003. Epub 2009 Aug 27.