Shi Bin, Wei Peng, Huang Xuelin
Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas, USA.
Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Stat Med. 2021 Feb 10;40(3):650-667. doi: 10.1002/sim.8794. Epub 2020 Nov 5.
Patients' longitudinal biomarker changing patterns are crucial factors for their disease progression. In this research, we apply functional principal component analysis techniques to extract these changing patterns and use them as predictors in landmark models for dynamic prediction. The time-varying effects of risk factors along a sequence of landmark times are smoothed by a supermodel to borrow information from neighbor time intervals. This results in more stable estimation and more clear demonstration of the time-varying effects. Compared with the traditional landmark analysis, simulation studies show our proposed approach results in lower prediction error rates and higher area under receiver operating characteristic curve (AUC) values, which indicate better ability to discriminate between subjects with different risk levels. We apply our method to data from the Framingham Heart Study, using longitudinal total cholesterol (TC) levels to predict future coronary heart disease (CHD) risk profiles. Our approach not only obtains the overall trend of biomarker-related risk profiles, but also reveals different risk patterns that are not available from the traditional landmark analyses. Our results show that high cholesterol levels during young ages are more harmful than those in old ages. This demonstrates the importance of analyzing the age-dependent effects of TC on CHD risk.
患者生物标志物的纵向变化模式是其疾病进展的关键因素。在本研究中，我们应用功能主成分分析技术来提取这些变化模式，并将其用作地标模型中的预测因子进行动态预测。通过一个超级模型对一系列地标时间点上危险因素的时变效应进行平滑处理，以从相邻时间间隔中借用信息。这导致了更稳定的估计和更清晰的时变效应展示。与传统的地标分析相比，模拟研究表明我们提出的方法导致更低的预测错误率和更高的受试者工作特征曲线下面积（AUC）值，这表明在区分不同风险水平的受试者方面具有更好的能力。我们将我们的方法应用于弗雷明汉心脏研究的数据，使用纵向总胆固醇（TC）水平来预测未来冠心病（CHD）风险概况。我们的方法不仅获得了与生物标志物相关的风险概况的总体趋势，还揭示了传统地标分析无法获得的不同风险模式。我们的结果表明，年轻时高胆固醇水平比老年时更有害。这证明了分析TC对CHD风险的年龄依赖性效应的重要性。
