Coeurjolly Jean-François, Nguile-Makao Moliere, Timsit Jean-François, Liquet Benoit
Laboratory Jean Kuntzmann, Department of Statistics, Grenoble University, 38041 Grenoble Cedex 9, France.
Biom J. 2012 Sep;54(5):600-16. doi: 10.1002/bimj.201100222. Epub 2012 Jul 31.
Attributable risk has become an important concept in clinical epidemiology. In this paper, we suggest to estimate the attributable risk of nosocomial infections using a multistate approach. Recently, a multistate model (called progressive disability model in the literature) has been developed in order to take into consideration both the time-dependency of the risk factor (e.g., nosocomial infections) and the presence of competing risks (e.g., death and discharge) at each time point. However, this approach does not take into account the possible heterogeneity of the study population. In this paper, we investigate an extension of this model and suggest an adjusted disability multistate model including covariates in each transition. This new multistate model has led us to define the concepts of overall and profiled attributable risk. We use a classical semiparametric approach to estimate the model and the new attributable risk. A simulation study is investigated and we show, in particular, that neglecting the presence of covariates when estimating the model can lead to an important bias. The methodology developed in this paper is applied to data on ventilator-associated pneumonia in 12 French intensive care units.
归因风险已成为临床流行病学中的一个重要概念。在本文中，我们建议使用多状态方法来估计医院感染的归因风险。最近，为了同时考虑风险因素（如医院感染）的时间依赖性以及每个时间点存在的竞争风险（如死亡和出院），已经开发了一种多状态模型（在文献中称为渐进性残疾模型）。然而，这种方法没有考虑研究人群可能存在的异质性。在本文中，我们研究了该模型的一种扩展，并提出了一种调整后的残疾多状态模型，在每个转移过程中纳入协变量。这种新的多状态模型使我们定义了总体归因风险和特征归因风险的概念。我们使用经典的半参数方法来估计模型和新的归因风险。进行了一项模拟研究，特别表明在估计模型时忽略协变量的存在可能会导致重大偏差。本文开发的方法应用于法国12个重症监护病房的呼吸机相关性肺炎数据。
