title: Assessment of biomarkers for risk prediction with nested case-control studies.
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.
  
  
  OBJECTIVE
  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.
authors.affiliation: aDepartment of Statistics and Actuarial Science, Simon Fraser University, Burnaby, BC, Canada.
authors.forename: Qian M
authors.initials: QM
authors.lastname: Zhou
authors.forename: Yingye
authors.initials: Y
authors.lastname: Zheng
authors.forename: Tianxi
authors.initials: T
authors.lastname: Cai
mesh_terms: D001172:Arthritis, Rheumatoid / Q000453:epidemiology; D015415:Biomarkers; D016022:Case-Control Studies; D002986:Clinical Trials as Topic / Q000379:methods; D006801:Humans; D011336:Probability; D012372:ROC Curve; D018570:Risk Assessment
chemical_list: D015415:Biomarkers