Dynamic Risk Prediction for Cervical Precancer Screening with Continuous and Binary Longitudinal Biomarkers

• Activity Number: 168 - Risk analysis and related topics
• Type: Contributed
• Date/Time: Tuesday, August 10, 2021 : 10:00 AM to 11:50 AM
• Sponsor: Section on Risk Analysis
• Author(s): Siddharth Roy* and Anindya Roy and Megan A. Clarke and Ana Gradissimo and Robert D. Burk and Nicolas Wentzensen and Paul S. Albert and Danping Liu
• Companies: National Cancer Institute and University of Maryland Baltimore County and U.S. Census Bureau/ UMBC and National Cancer Institute and Albert Einstein College of Medicine and Albert Einstein College of Medicine and National Cancer Institute and National Cancer Institute and National Cancer Institute/National Institutes of Health
• Keywords: Cervical precancer; Dynamic risk prediction; Joint model; Longitudinal biomarkers; Shared Random Effects

Abstract

We are interested in developing a dynamic risk model that uses multiple longitudinal biomarkers to improve precancer risk estimation. Currently, Pap cytology is used to identify HPV-positive (HPV+) women at high-risk of cervical precancer, but it lacks accuracy and reproducibility. HPV DNA methylation is closely linked to the carcinogenic process and may show promise of improved risk stratification. 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 closed-form 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 5-year risk estimates that may inform clinical decision-making. Applying the method to longitudinally measured HPV methylation data may/can improve risk stratification of HPV+ women.