Barnett Adrian, Graves Nick
Institute of Health and Biomedical Innovation, Queensland University of Technology, 60 Musk Avenue, Kelvin Grove Urban Village, Kelvin Grove, Queensland 4059, Australia.
Crit Care. 2008;12(2):134. doi: 10.1186/cc6840. Epub 2008 Apr 11.
New statistical models for analysing survival data in an intensive care unit context have recently been developed. Two models that offer significant advantages over standard survival analyses are competing risks models and multistate models. Wolkewitz and colleagues used a competing risks model to examine survival times for nosocomial pneumonia and mortality. Their model was able to incorporate time-dependent covariates and so examine how risk factors that changed with time affected the chances of infection or death. We briefly explain how an alternative modelling technique (using logistic regression) can more fully exploit time-dependent covariates for this type of data.
最近开发了用于分析重症监护病房环境下生存数据的新统计模型。与标准生存分析相比具有显著优势的两种模型是竞争风险模型和多状态模型。沃尔克维茨及其同事使用竞争风险模型来研究医院获得性肺炎的生存时间和死亡率。他们的模型能够纳入随时间变化的协变量,从而研究随时间变化的风险因素如何影响感染或死亡的几率。我们简要解释一种替代建模技术(使用逻辑回归)如何能更充分地利用这类数据的随时间变化的协变量。