Cai T, Tian L, Lloyd-Jones D, Wei L J
Department of Biostatistics, Harvard University, Boston, MA, 02115, USA,
Lifetime Data Anal. 2013 Oct;19(4):547-67. doi: 10.1007/s10985-013-9272-6. Epub 2013 Jun 27.
Suppose that we need to classify a population of subjects into several well-defined ordered risk categories for disease prevention or management with their "baseline" risk factors/markers. In this article, we present a systematic approach to identify subjects using their conventional risk factors/markers who would benefit from a new set of risk markers for more accurate classification. Specifically for each subgroup of individuals with the same conventional risk estimate, we present inference procedures for the reclassification and the corresponding correct re-categorization rates with the new markers. We then apply these new tools to analyze the data from the Cardiovascular Health Study sponsored by the US National Heart, Lung, and Blood Institute. We used Framingham risk factors plus the information of baseline anti-hypertensive drug usage to identify adult American women who may benefit from the measurement of a new blood biomarker, CRP, for better risk classification in order to intensify prevention of coronary heart disease for the subsequent 10 years.
假设我们需要根据受试者的“基线”风险因素/标志物,将一群受试者分类到几个明确界定的有序风险类别中,以进行疾病预防或管理。在本文中,我们提出了一种系统方法,利用受试者的传统风险因素/标志物来识别那些将从一组新的风险标志物中受益的受试者,以便进行更准确的分类。具体而言,对于具有相同传统风险估计的每个个体亚组,我们给出了重新分类的推断程序以及使用新标志物时相应的正确重新分类率。然后,我们应用这些新工具来分析由美国国立心肺血液研究所资助的心血管健康研究的数据。我们使用弗明汉姆风险因素加上基线抗高血压药物使用信息,来识别可能从测量一种新的血液生物标志物CRP中受益的成年美国女性,以便在随后的10年中更好地进行风险分类,从而加强冠心病的预防。