Suppr超能文献

评估低潮气量通气对急性肺损伤患者生存率的因果效应。

Estimating the Causal Effect of Low Tidal Volume Ventilation on Survival in Patients with Acute Lung Injury.

作者信息

Wang Weiwei, Scharfstein Daniel, Wang Chenguang, Daniels Michael, Needham Dale, Brower Roy

机构信息

University of Texas Health Science Center at Houston, Houston, TX U.S.A.

出版信息

J R Stat Soc Ser C Appl Stat. 2011 Aug 1;60(4):475-496. doi: 10.1111/j.1467-9876.2010.00757.x.

Abstract

Acute lung injury (ALI) is a condition characterized by acute onset of severe hypoxemia and bilateral pulmonary infiltrates. ALI patients typically require mechanical ventilation in an intensive care unit. Low tidal volume ventilation (LTVV), a time-varying dynamic treatment regime, has been recommended as an effective ventilation strategy. This recommendation was based on the results of the ARMA study, a randomized clinical trial designed to compare low vs. high tidal volume strategies (The Acute Respiratory Distress Syndrome Network, 2000) . After publication of the trial, some critics focused on the high non-adherence rates in the LTVV arm suggesting that non-adherence occurred because treating physicians felt that deviating from the prescribed regime would improve patient outcomes. In this paper, we seek to address this controversy by estimating the survival distribution in the counterfactual setting where all patients assigned to LTVV followed the regime. Inference is based on a fully Bayesian implementation of Robins' (1986) G-computation formula. In addition to re-analyzing data from the ARMA trial, we also apply our methodology to data from a subsequent trial (ALVEOLI), which implemented the LTVV regime in both of its study arms and also suffered from non-adherence.

摘要

急性肺损伤(ALI)是一种以急性严重低氧血症和双侧肺部浸润为特征的病症。ALI患者通常需要在重症监护病房进行机械通气。低潮气量通气(LTVV)是一种随时间变化的动态治疗方案,已被推荐为一种有效的通气策略。这一推荐基于ARMA研究的结果,该研究是一项随机临床试验,旨在比较低潮气量与高潮气量策略(急性呼吸窘迫综合征网络,2000年)。该试验发表后,一些批评者关注LTVV组的高不依从率,认为不依从的发生是因为治疗医生觉得偏离规定方案会改善患者预后。在本文中,我们试图通过估计在所有分配到LTVV的患者都遵循该方案的反事实情况下的生存分布来解决这一争议。推断基于罗宾斯(1986年)G计算公式完全贝叶斯实现。除了重新分析ARMA试验的数据外,我们还将我们的方法应用于后续试验(肺泡试验)的数据,该试验在其两个研究组中都实施了LTVV方案,并且也存在不依从情况。

相似文献

引用本文的文献

2
7
A Bayesian approach to the g-formula.贝叶斯方法在 g 公式中的应用。
Stat Methods Med Res. 2018 Oct;27(10):3183-3204. doi: 10.1177/0962280217694665. Epub 2017 Mar 2.

本文引用的文献

4
Marginal Mean Models for Dynamic Regimes.动态状态的边际均值模型。
J Am Stat Assoc. 2001 Dec 1;96(456):1410-1423. doi: 10.1198/016214501753382327.
7
Inference for non-regular parameters in optimal dynamic treatment regimes.最优动态治疗方案中非正则参数的推断。
Stat Methods Med Res. 2010 Jun;19(3):317-43. doi: 10.1177/0962280209105013. Epub 2009 Jul 16.
10
Demystifying optimal dynamic treatment regimes.揭开最优动态治疗方案的神秘面纱。
Biometrics. 2007 Jun;63(2):447-55. doi: 10.1111/j.1541-0420.2006.00686.x.

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