In clinical research and practice, landmark models are commonly used to predict the risk of an adverse future event, using patients' longitudinal biomarker data as predictors. However, these data are often observable only at intermittent visits, making their measurement times irregularly spaced and unsynchronized across different subjects. This poses challenges to conducting dynamic prediction at any post‐baseline time. A simple solution is the last‐value‐carry‐forward method, but this may result in bias for the risk model estimation and prediction. Another option is to jointly model the longitudinal and survival processes with a shared random effects model. However, when dealing with multiple biomarkers, this approach often results in high‐dimensional integrals without a closed‐form solution, and thus the computational burden limits its software development and practical use. In this article, we propose to process the longitudinal data by functional principal component analysis techniques, and then use the processed information as predictors in a class of flexible linear transformation models to predict the distribution of residual time‐to‐event occurrence. The measurement schemes for multiple biomarkers are allowed to be different within subject and across subjects. Dynamic prediction can be performed in a real‐time fashion. The advantages of our proposed method are demonstrated by simulation studies. We apply our approach to the African American Study of Kidney Disease and Hypertension, predicting patients' risk of kidney failure or death by using four important longitudinal biomarkers for renal functions.
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.
more » « less- NSF-PAR ID:
- 10454288
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Statistics in Medicine
- Volume:
- 40
- Issue:
- 3
- ISSN:
- 0277-6715
- Format(s):
- Medium: X Size: p. 650-667
- Size(s):
- p. 650-667
- Sponsoring Org:
- National Science Foundation
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