Assessment of biomarkers for risk prediction with nested case

Abstract

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.

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

Abstract

Background

Purpose

Methods

Results

Conclusions

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References