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部分暴露方案的边际结构模型。

Marginal structural models for partial exposure regimes.

作者信息

Vansteelandt Stijn, Mertens Karl, Suetens Carl, Goetghebeur Els

机构信息

Department of Applied Mathematics and Computer Science, Ghent University, Ghent, Belgium.

出版信息

Biostatistics. 2009 Jan;10(1):46-59. doi: 10.1093/biostatistics/kxn012. Epub 2008 May 23.

Abstract

Intensive care unit (ICU) patients are highly susceptible to hospital-acquired infections due to their poor health and many invasive therapeutic treatments. The effect on mortality of acquiring such infections is, however, poorly understood. Our goal is to quantify this using data from the National Surveillance Study of Nosocomial Infections in ICUs (Belgium). This is challenging because of the presence of time-dependent confounders, such as mechanical ventilation, which lie on the causal path from infection to mortality. Standard statistical analyses may be severely misleading in such settings and have shown contradictory results. Inverse probability weighting for marginal structural models may instead be used but is not directly applicable because these models parameterize the effect of acquiring infection on a given day in ICU, versus "never" acquiring infection in ICU, and this is ill-defined when ICU discharge precedes that day. Additional complications arise from the informative censoring of the survival time by hospital discharge and the instability of the inverse weighting estimation procedure. We accommodate this by introducing a new class of marginal structural models for so-called partial exposure regimes. These describe the effect on the hazard of death of acquiring infection on a given day s, versus not acquiring infection "up to that day," had patients stayed in the ICU for at least s days.

摘要

重症监护病房(ICU)的患者由于健康状况不佳以及接受许多侵入性治疗,极易发生医院获得性感染。然而,人们对感染此类疾病对死亡率的影响了解甚少。我们的目标是利用来自比利时ICU医院感染国家监测研究的数据对此进行量化。这具有挑战性,因为存在时间依赖性混杂因素,如机械通气,它处于从感染到死亡的因果路径上。在这种情况下,标准统计分析可能会产生严重误导,并且已经显示出相互矛盾的结果。相反,可以使用边际结构模型的逆概率加权法,但它不能直接应用,因为这些模型参数化了在ICU某一天获得感染的影响,与“从未”在ICU获得感染相比,而当ICU出院先于该日时,这是不明确的。生存时间因出院而产生的信息性删失以及逆加权估计程序的不稳定性会引发其他复杂问题。我们通过引入一类新的边际结构模型来处理所谓的部分暴露情况,从而解决这一问题。这些模型描述了如果患者在ICU至少停留s天,那么在给定的第s天获得感染与“截至该日”未获得感染相比,对死亡风险的影响。

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