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How unmeasured confounding in a competing risks setting can affect treatment effect estimates in observational studies

机译：在竞争性风险环境中无法衡量的混淆如何影响观察研究中的治疗效果估计

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摘要

BackgroundAnalysis of competing risks is commonly achieved through a cause specific or a subdistribution framework using Cox or Fine & Gray models, respectively. The estimation of treatment effects in observational data is prone to unmeasured confounding which causes bias. There has been limited research into such biases in a competing risks framework.

机译：背景通常，分别使用Cox或Fine＆Gray模型通过特定于原因或子分布框架来分析竞争风险。在观察数据中对治疗效果的估计容易产生无法衡量的混淆，从而引起偏差。在竞争风险框架中，对此类偏见的研究很少。

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期刊名称 BMC Medical Research Methodology
作者
Michael Andrew Barrowman; Niels Peek; Mark Lambie; Glen Philip Martin; Matthew Sperrin;
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作者单位

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年(卷),期 2019(19),-1
年度 2019
页码 166
总页数 11
原文格式 PDF
正文语种
中图分类医院、综合医院;中医院;
关键词
Competing risks Unmeasured confounding Simulation study Observation studies;

机译：竞争风险;不可测混杂;模拟研究;观察研究;

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专利

1. How unmeasured confounding in a competing risks setting can affect treatment effect estimates in observational studies [J] . Michael Andrew Barrowman, Niels Peek, Mark Lambie, BMC Medical Research Methodology . 2019,第1期

机译：在竞争性风险环境中无法衡量的混淆如何影响观察研究中的治疗效果估计
2. Estimating the causal effect of treatment in observational studies with survival time end points and unmeasured confounding [J] . Jaeun Choi, A. James OMalley Journal of the royal statistical society . 2017,第PTa1期

机译：用生存时间终点和无法衡量的混淆估计观察研究中治疗的因果关系
3. How to control for unmeasured confounding in an observational time-to-event study with exposure incidence information: the treatment choice Cox model [J] . Troendle James, Leifer Eric, Zhang Zhiwei, Statistics in medicine . 2017,第23期

机译：如何控制未测量的混淆，在观察时间研究中具有曝光发病率信息：治疗选择COX模型
4. Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders [C] . Ioana Bica, Ahmed M. Alaa, Mihaela van der Schaar International Conference on Machine Learning . 2021

机译：时间序列DeconFounder：在隐藏混乱的存在下估算治疗效果随着时间的推移
5. Modeling and inferential approaches for treatment response data in cross-section and panel settings with confounding on unobservables. [D] . Jacobi, Liana. 2005

机译：横截面和面板设置中的处理响应数据的建模和推论方法，以及对不可观察对象的混淆。
6. Estimating the Causal Effect of Treatment in Observational Studies with Survival Time Endpoints and Unmeasured Confounding [O] . Jaeun Choi, A. James OMalley -1

机译：估计生存时间终点和无法衡量的混淆的观察性研究中治疗的因果关系
7. How unmeasured confounding in a competing risks setting can affect treatment effect estimates in observational studies [O] . Michael Andrew Barrowman, Niels Peek, Mark Lambie, 2019

机译：在竞争风险环境中的混淆是如何影响观察研究中的治疗效果估算
8. Estimating Dependent Life Lengths, with Applications to the Theory of Competing Risks. [R] . Langberg, N., Proschan, F., Quinzi, A. J. 1977

机译：估计相关寿命长度，并应用于竞争风险理论。

How unmeasured confounding in a competing risks setting can affect treatment effect estimates in observational studies

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