Immortal time bias in observational studies of time-to-event outcomes
Immortal time bias in observational studies of time-to-event outcomes
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DOI：
10.1016/j.jcrc.2016.07.017
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发表时间：
2016-12-01
影响因子：
3.7
通讯作者：
Fowler, Robert
中科院分区：
文献类型：
--
作者：
Jones, Mark;Fowler, Robert
目的：本研究的目的是通过模拟和示例来显示，当使用不适当的分析时，不朽时间偏差的大小和方向。材料和方法：我们使用流感危重患者的示例数据集和模拟研究，比较了事件发生时间结果的观察性研究的 4 种分析方法：逻辑回归、标准 Cox 模型、界标分析和时间依赖性 Cox 模型。结果：对于示例数据集，逻辑回归、标准 Cox 模型和界标分析均显示一些证据表明，奥司他韦治疗可以保护流感危重患者免于死亡。然而，当使用时间依赖性 Cox 模型考虑治疗暴露的时间依赖性时，不再有证据表明治疗具有保护作用。模拟研究表明，在各种情况下，时间相关的Cox模型始终提供无偏的治疗效果估计，而标准Cox模型会导致有利于治疗的偏差。逻辑回归和里程碑分析也可能导致偏差。结论：为了最大限度地减少生存结果观察性研究中永久时间偏差的风险，我们强烈建议将时间依赖性暴露作为时间依赖性变量纳入基于危险的分析中。 (C) 2016 Elsevier Inc. 保留所有权利。
Purpose: The purpose of the study is to show, through simulation and example, the magnitude and direction of immortal time bias when an inappropriate analysis is used.Materials and methods: We compare 4 methods of analysis for observational studies of time-to-event outcomes: logistic regression, standard Cox model, landmark analysis, and time-dependent Cox model using an example data set of patients critically ill with influenza and a simulation study.Results: For the example data set, logistic regression, standard Cox model, and landmark analysis all showed some evidence that treatment with oseltamivir provides protection from mortality in patients critically ill with influenza. However, when the time-dependent nature of treatment exposure is taken account of using a time-dependent Cox model, there is no longer evidence of a protective effect of treatment. The simulation study showed that, under various scenarios, the time-dependent Cox model consistently provides unbiased treatment effect estimates, whereas standard Cox model leads to bias in favor of treatment. Logistic regression and landmark analysis may also lead to bias.Conclusions: To minimize the risk of immortal time bias in observational studies of survival outcomes, we strongly suggest time-dependent exposures be included as time-dependent variables in hazard-based analyses. (C) 2016 Elsevier Inc. All rights reserved.