Assessment of biomarkers for risk prediction with nested case–control studies

Background Accurate risk prediction plays a key role in disease prevention and disease management; emergence of new biomarkers may lead to an important question about how much improvement in prediction accuracy it would achieve by adding the new markers into the existing risk prediction tools. Purpo...

Full description

Saved in:
Bibliographic Details
Published in:Clinical trials (London, England) England), 2013-10, Vol.10 (5), p.677-679
Main Authors: Zhou, Qian M, Zheng, Yingye, Cai, Tianxi
Format: Article
Language:English
Subjects:
Arthritis, Rheumatoid - epidemiology
Biomarkers
Case-Control Studies
Clinical Trials as Topic - methods
Genetic markers
Humans
Probability
Rheumatoid arthritis
Risk Assessment
ROC Curve
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Background Accurate risk prediction plays a key role in disease prevention and disease management; emergence of new biomarkers may lead to an important question about how much improvement in prediction accuracy it would achieve by adding the new markers into the existing risk prediction tools. Purpose In large prospective cohort studies, the standard full-cohort design, requiring marker measurement on the entire cohort, may be infeasible due to cost and low rate of the clinical condition of interest. To overcome such difficulties, nested case–control (NCC) studies provide cost-effective alternatives but bring about challenges in statistical analyses due to complex data sets generated. Methods To evaluate prognostic accuracy of a risk model, Cai and Zheng proposed a class of nonparametric inverse probability weighting (IPW) estimators for accuracy measures in the time-dependent receiver operating characteristic curve analysis. To accommodate a three-phase NCC design in Nurses’ Health Study, we extend the double IPW estimators of Cai and Zheng to develop risk prediction models under time-dependent generalized linear models and evaluate the incremental values of new biomarkers and genetic markers. Results Our results suggest that aggregating the information from both the genetic markers and biomarkers substantially improves the accuracy for predicting 5-year and 10-year risks of rheumatoid arthritis. Conclusions Our method provided robust procedures to evaluate the incremental value of new biomarkers allowing for complex sampling designs.
ISSN:1740-7745
1740-7753
DOI:10.1177/1740774513498321