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.

中文翻译：

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基于功能主成分的地标分析纵向胆固醇谱对冠心病风险的影响

患者的纵向生物标志物变化模式是其疾病进展的关键因素。在这项研究中，我们应用功能主成分分析技术来提取这些变化的模式，并将它们用作地标模型中的预测因子以进行动态预测。超级模型平滑了风险因素沿一系列地标时间的时变效应，以从相邻时间间隔借用信息。这导致更稳定的估计和更清晰的时变效应演示。与传统的地标分析相比，模拟研究表明，我们提出的方法具有较低的预测错误率和较高的受试者工作特征曲线 (AUC) 值下面积，这表明具有更好地区分不同风险水平的受试者的能力。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) 水平来预测未来的冠心病 (CHD) 风险概况。我们的方法不仅获得了生物标志物相关风险概况的总体趋势，而且还揭示了传统地标分析无法获得的不同风险模式。我们的研究结果表明，年轻时的高胆固醇水平比老年时的高胆固醇水平更有害。这证明了分析 TC 对冠心病风险的年龄依赖性影响的重要性。但也揭示了传统地标分析无法提供的不同风险模式。我们的研究结果表明，年轻时的高胆固醇水平比老年时的高胆固醇水平更有害。这证明了分析 TC 对冠心病风险的年龄依赖性影响的重要性。但也揭示了传统地标分析无法提供的不同风险模式。我们的研究结果表明，年轻时的高胆固醇水平比老年时的高胆固醇水平更有害。这证明了分析 TC 对冠心病风险的年龄依赖性影响的重要性。