title: Quantifying the value of biomarkers for predicting mortality.
abstract: OBJECTIVE
  In light of widespread interest in the prognostic value of biomarkers, we apply three discrimination measures to evaluate the incremental value of biomarkers--beyond self-reported measures--for predicting all-cause mortality. We assess whether all three measures--area under the receiver-operating characteristic curve, continuous net reclassification improvement, and integrated discrimination improvement--lead to the same conclusions.
  
  
  METHODS
  We use longitudinal data from a nationally representative sample of older Taiwanese (n = 639, aged 54 or older in 2000, examined in 2000 and 2006, with mortality follow-up through 2011). We estimate age-specific mortality using a Gompertz hazard model.
  
  
  RESULTS
  The broad conclusions are consistent across the three discrimination measures and support the inclusion of biomarkers, particularly inflammatory markers, in household surveys. Although the rank ordering of individual biomarkers varies across discrimination measures, the following is true for all three: interleukin-6 is the strongest predictor, the other three inflammatory markers make the top 10, and homocysteine ranks second or third.
  
  
  CONCLUSIONS
  The consistency of most of our findings across metrics should provide comfort to researchers using discrimination measures to evaluate the prognostic value of biomarkers. However, because the degree of consistency varies with the level of detail inherent in the research question, we recommend that researchers confirm results with multiple discrimination measures.
authors.affiliation: Office of Population Research, Woodrow Wilson School of Public and International Affair, Princeton University, Princeton, NJ.
authors.forename: Noreen
authors.initials: N
authors.lastname: Goldman
authors.affiliation: Center for Population and Health, Georgetown University, Washington, D.C.. Electronic address: dag77@georgetown.edu.
authors.forename: Dana A
authors.initials: DA
authors.lastname: Glei
mesh_terms: D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D015415:Biomarkers / Q000097:blood*; D005260:Female; D005544:Forecasting / Q000379:methods*; D006710:Homocysteine / Q000097:blood*; D006801:Humans; D007249:Inflammation / Q000097:blood*; D015850:Interleukin-6 / Q000097:blood*; D008137:Longitudinal Studies; D008297:Male; D008875:Middle Aged; D009026:Mortality / Q000639:trends*; D012372:ROC Curve; D018570:Risk Assessment / Q000379:methods*; D013624:Taiwan
chemical_list: D015415:Biomarkers; D015850:Interleukin-6; D006710:Homocysteine
keywords: Biological markers; Discrimination; Inflammation; Mortality; Prognosis; Taiwan