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长期随访的随机试验分析。

Analysis of randomised trials with long-term follow-up.

机构信息

Neuroscience Research Australia (NeuRA), Sydney, Australia.

The University of New South Wales, Sydney, Australia.

出版信息

BMC Med Res Methodol. 2018 May 29;18(1):48. doi: 10.1186/s12874-018-0499-5.

Abstract

Randomised trials with long-term follow-up can provide estimates of the long-term effects of health interventions. However, analysis of long-term outcomes in randomised trials may be complicated by problems with the administration of treatment such as non-adherence, treatment switching and co-intervention, and problems obtaining outcome measurements arising from loss to follow-up and death of participants. Methods for dealing with these issues that involve conditioning on post-randomisation variables are unsatisfactory because they may involve the comparison of non-exchangeable groups and generate estimates that do not have a valid causal interpretation. We describe approaches to analysis that potentially provide estimates of causal effects when such issues arise. Brief descriptions are provided of the use of instrumental variable and propensity score methods in trials with imperfect adherence, marginal structural models and g-estimation in trials with treatment switching, mixed longitudinal models and multiple imputation in trials with loss to follow-up, and a sensitivity analysis that can be used when trial follow-up is truncated by death or other events. Clinical trialists might consider these methods both at the design and analysis stages of randomised trials with long-term follow-up.

摘要

随机试验的长期随访结果可以评估健康干预的长期效果。然而,分析随机试验的长期结局可能会受到治疗管理问题的影响,如不依从、治疗转换和联合干预,以及由于随访丢失和参与者死亡而导致的结局测量问题。涉及对随机化后变量进行条件分析的方法并不令人满意,因为这可能涉及到不可交换组的比较,并产生没有有效因果解释的估计值。我们描述了当出现这些问题时,用于分析的潜在因果效应估计方法。简要介绍了在不依从性试验中使用工具变量和倾向评分方法、在治疗转换试验中使用边缘结构模型和 g 估计、在随访丢失试验中使用混合纵向模型和多重插补,以及当试验随访因死亡或其他事件而截断时可以使用的敏感性分析。临床研究者在进行长期随访的随机试验的设计和分析阶段都可以考虑这些方法。

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本文引用的文献

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TREATMENT SWITCHING: STATISTICAL AND DECISION-MAKING CHALLENGES AND APPROACHES.
Int J Technol Assess Health Care. 2016 Jan;32(3):160-6. doi: 10.1017/S026646231600026X.
3
Does Cox analysis of a randomized survival study yield a causal treatment effect?
Lifetime Data Anal. 2015 Oct;21(4):579-93. doi: 10.1007/s10985-015-9335-y. Epub 2015 Jun 24.
4
Beyond intention to treat: what is the right question?
Clin Trials. 2014 Feb;11(1):28-37. doi: 10.1177/1740774513504151. Epub 2013 Oct 3.
5
Methods for dealing with time-dependent confounding.
Stat Med. 2013 Apr 30;32(9):1584-618. doi: 10.1002/sim.5686. Epub 2012 Dec 3.
6
Missing data in clinical studies: issues and methods.
J Clin Oncol. 2012 Sep 10;30(26):3297-303. doi: 10.1200/JCO.2011.38.7589. Epub 2012 May 29.
7
Beyond the intention-to-treat in comparative effectiveness research.
Clin Trials. 2012 Feb;9(1):48-55. doi: 10.1177/1740774511420743. Epub 2011 Sep 23.
8
A simple method for principal strata effects when the outcome has been truncated due to death.
Am J Epidemiol. 2011 Apr 1;173(7):745-51. doi: 10.1093/aje/kwq418. Epub 2011 Feb 25.
9
Causal inference from longitudinal studies with baseline randomization.
Int J Biostat. 2008 Oct 19;4(1):Article 22. doi: 10.2202/1557-4679.1117.
10
On the use of propensity scores in principal causal effect estimation.
Stat Med. 2009 Oct 15;28(23):2857-75. doi: 10.1002/sim.3669.

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