Semiparametric isotonic regression analysis for risk assessment under nested case-control and case-cohort designs

Two-phase sampling designs, including nested case-control and case-cohort designs, are frequently utilized in large cohort studies involving expensive biomarkers. To analyze data from two-phase designs with a binary outcome, parametric models such as logistic regression are often adopted. However, w...

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Bibliographic Details
Published in:Statistical methods in medical research Vol. 29; no. 8; p. 2328
Main Authors: Li, Wen, Li, Ruosha, Feng, Ziding, Ning, Jing
Format: Journal Article
Language:English
Published: England 01.08.2020
Subjects:
Case-Control Studies
Cohort Studies
Humans
Male
Models, Statistical
Regression Analysis
Risk Assessment
isotonic regression
risk assessment
Case-cohort design
nested case-control design
inverse probability weighting
two-phase studies
ISSN:1477-0334, 1477-0334
Online Access:Get more information
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Summary:Two-phase sampling designs, including nested case-control and case-cohort designs, are frequently utilized in large cohort studies 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 easy-to-implement computational procedure that combines the pool-adjacent violators algorithm with inverse probability weighting. The asymptotic properties are 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.
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ISSN:1477-0334
1477-0334
DOI:10.1177/0962280219893389