Abstract
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.