Semiparametric Isotonic Regression Analysis for Risk Assessment Under Two-Phase Sampling Designs

• Activity Number: 304 - Risk Applications for Disease, Toxicology, and Biomarker Modeling
• Type: Contributed
• Date/Time: Tuesday, July 30, 2019 : 8:30 AM to 10:20 AM
• Sponsor: Section on Risk Analysis
• Author(s): Wen Li* and Ruosha Li and Ziding Feng and Jing Ning
• Companies: The University of Texas School of Public Health and The University of Texas School of Public Health and Fred Hutchinson Cancer Research Center and The University of Texas MD Anderson Cancer Center
• Keywords: Case-cohort design; Inverse probability weighting; Isotonic regression; Nested case-control design; Risk prediction; Two-phase studies

Abstract

Two-phase sampling designs, including nested case-control and case-cohort designs, are frequently utilized in large cohort studies for risk assessment and prediction involving expensive biomarkers. To analyze data from two-phase designs with a binary outcome, parametric models such as logistic regression are often adopted. However, when the model assumptions are not valid, parametric models may lead to biased estimation and risk evaluation. In this paper, we propose a robust semiparametric regression model for binary outcomes and an easily implementable computational procedure that combines the pool-adjacent violators algorithm with inverse probability weighting. The asymptotic properties have been established, including consistency and the convergence rate. Simulation studies show that the proposed method performs well and is more robust than logistic regression methods. We demonstrate the application of the proposed method to real data from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial.