DYNAMIC RISK PREDICTION FOR CERVICAL PRECANCER SCREENING WITH CONTINUOUS AND BINARY LONGITUDINAL BIOMARKERS

• 成果类型: Article
• 署名作者: Roy, Siddharth; Roy, Anindya; Clarke, Megan A.; Gradissimo, Ana; Burk, Robert D.; Wentzensen, Nicolas; Albert, Paul S.; Liu, Danping
• 署名单位: National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); NIH National Cancer Institute- Division of Cancer Epidemiology & Genetics; University System of Maryland; University of Maryland Baltimore County; National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); NIH National Cancer Institute- Division of Cancer Epidemiology & Genetics; Memorial Sloan Kettering Cancer Center; Montefiore Medical Center; Albert Einstein College of Medicine; Yeshiva University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/23-AOAS1788
• 发表日期: 2024
• 页码: 246-265
• 关键词: to-event data; joint models; survival; management

摘要

Dynamic risk prediction that incorporates longitudinal measurements of biomarkers is useful in identifying high-risk patients for better clinical management. Our work is motivated by the prediction of cervical precancers. Currently, Pap cytology is used to identify HPV-positive (HPV+) women at high-risk of cervical precancer, but cytology lacks accuracy and reproducibility. Molecular markers, like HPV DNA methylation, that are closely linked to the carcinogenic process show promise of improved risk stratification. We are interested in developing a dynamic risk model that uses all longitudinal biomarker information to improve precancer risk estimation. We propose a joint model to link both the continuous methylation biomarker and a binary cytology biomarker to the time to precancer outcome using shared random effects. The model uses a discretization of the time scale to allow for closedform likelihood expressions, thereby avoiding potential high dimensional integration of the random effects. The method handles an interval-censored time-to-event outcome, due to intermittent clinical visits, incorporates sampling weights to deal with stratified sampling data and can provide immediate and five-year risk estimates that may inform clinical decision-making. Applying the method to longitudinally measured HPV methylation data improves risk stratification for triage of HPV+ women.