Yang Lili, Yu Menggang, Gao Sujuan
Eli Lilly and Company, Indianapolis, IN, 46285, U.S.A.
Department of Biostatistics and Medical Informatics, University of Wisconsin School of Medicine and Population Health, Madison, Wisconsin.
Stat Med. 2016 Apr 15;35(8):1299-314. doi: 10.1002/sim.6754. Epub 2015 Oct 5.
In the last decade, few topics in the area of cardiovascular disease (CVD) research have received as much attention as risk prediction. One of the well-documented risk factors for CVD is high blood pressure (BP). Traditional CVD risk prediction models consider BP levels measured at a single time and such models form the basis for current clinical guidelines for CVD prevention. However, in clinical practice, BP levels are often observed and recorded in a longitudinal fashion. Information on BP trajectories can be powerful predictors for CVD events. We consider joint modeling of time to coronary artery disease and individual longitudinal measures of systolic and diastolic BPs in a primary care cohort with up to 20 years of follow-up. We applied novel prediction metrics to assess the predictive performance of joint models. Predictive performances of proposed joint models and other models were assessed via simulations and illustrated using the primary care cohort.
在过去十年中，心血管疾病（CVD）研究领域很少有主题像风险预测那样受到如此多的关注。CVD的一个有充分记录的风险因素是高血压（BP）。传统的CVD风险预测模型考虑单次测量的BP水平，这些模型构成了当前CVD预防临床指南的基础。然而，在临床实践中，BP水平通常是以纵向方式观察和记录的。关于BP轨迹的信息可以成为CVD事件的有力预测指标。我们考虑在一个有长达20年随访的初级保健队列中，对冠心病发病时间与收缩压和舒张压的个体纵向测量进行联合建模。我们应用了新颖的预测指标来评估联合模型的预测性能。通过模拟评估了所提出的联合模型和其他模型的预测性能，并使用初级保健队列进行了说明。
