Reflection on modern methods: trial emulation in the presence of immortal-time bias. Assessing the benefit of major surgery for elderly lung cancer patients using observational data.
Reflection on modern methods: trial emulation in the presence of immortal-time bias. Assessing the benefit of major surgery for elderly lung cancer patients using observational data.
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DOI：
10.1093/ije/dyaa057
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发表时间：
2020-10-01
影响因子：
7.7
通讯作者：
Leyrat C
中科院分区：
文献类型：
--
作者：
Maringe C;Benitez Majano S;Exarchakou A;Smith M;Rachet B;Belot A;Leyrat C
获取真实世界的证据对于支持卫生政策至关重要，但观察性研究往往存在严重的偏差。最近提出了一种方法，以克服在模拟试验框架内的混淆和不朽的时间偏见。本教程逐步介绍了模拟试验的设计和分析，以及R和STATA代码，以便于在实践中使用。这些步骤包括：(1)指定目标试验和纳入标准；(2)克隆患者；(3)确定审查和存活时间；(4)估计权重，以说明设计引入的信息审查；以及(5)分析这些数据。这些步骤用观察数据说明，以评估70-89岁被诊断为早期肺癌的患者接受手术的好处。由于治疗组之间患者特征的严重失衡(手术是/否)，对初始队列的天真的Kaplan-Meier生存分析严重高估了手术对一年存活率的好处(差异22%)，当信息审查被忽略时，克隆数据集的生存分析也是如此(差异17%)。相比之下，估计的权重充分消除了协变量的不平衡。加权分析仍然显示，手术对老年肺癌患者的1年生存率有好处，尽管幅度较小(差异11%)。作为对CERBOT工具的补充，本教程介绍了如何在存在不朽时间偏差的情况下使用观测数据进行模拟试验。这种方法的优势在于它的透明度和非专业人士很容易理解的原则。
Acquiring real-world evidence is crucial to support health policy, but observational studies are prone to serious biases. An approach was recently proposed to overcome confounding and immortal-time biases within the emulated trial framework. This tutorial provides a step-by-step description of the design and analysis of emulated trials, as well as R and Stata code, to facilitate its use in practice. The steps consist in: (i) specifying the target trial and inclusion criteria; (ii) cloning patients; (iii) defining censoring and survival times; (iv) estimating the weights to account for informative censoring introduced by design; and (v) analysing these data. These steps are illustrated with observational data to assess the benefit of surgery among 70–89-year-old patients diagnosed with early-stage lung cancer. Because of the severe unbalance of the patient characteristics between treatment arms (surgery yes/no), a naïve Kaplan-Meier survival analysis of the initial cohort severely overestimated the benefit of surgery on 1-year survival (22% difference), as did a survival analysis of the cloned dataset when informative censoring was ignored (17% difference). By contrast, the estimated weights adequately removed the covariate imbalance. The weighted analysis still showed evidence of a benefit, though smaller (11% difference), of surgery among older lung cancer patients on 1-year survival. Complementing the CERBOT tool, this tutorial explains how to proceed to conduct emulated trials using observational data in the presence of immortal-time bias. The strength of this approach is its transparency and its principles that are easily understandable by non-specialists.
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