Open AccessSemiparametric isotonic regression analysis for risk assessment under nested case-control and case-cohort designs
Author(s) -
Wen Li,
Ruosha Li,
Ziding Feng,
Jing Ning
Publication year - 2019
Publication title -
statistical methods in medical research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.952
H-Index - 85
eISSN - 1477-0334
pISSN - 0962-2802
DOI - 10.1177/0962280219893389
Subject(s) - inverse probability weighting , logistic regression , computer science , nested case control study , statistics , parametric statistics , weighting , semiparametric regression , semiparametric model , consistency (knowledge bases) , econometrics , estimator , cohort , mathematics , artificial intelligence , medicine , radiology
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.