title: Derivation and assessment of risk prediction models using case-cohort data.
abstract: BACKGROUND
  Case-cohort studies are increasingly used to quantify the association of novel factors with disease risk. Conventional measures of predictive ability need modification for this design. We show how Harrell's C-index, Royston's D, and the category-based and continuous versions of the net reclassification index (NRI) can be adapted.
  
  
  METHODS
  We simulated full cohort and case-cohort data, with sampling fractions ranging from 1% to 90%, using covariates from a cohort study of coronary heart disease, and two incidence rates. We then compared the accuracy and precision of the proposed risk prediction metrics.
  
  
  RESULTS
  The C-index and D must be weighted in order to obtain unbiased results. The NRI does not need modification, provided that the relevant non-subcohort cases are excluded from the calculation. The empirical standard errors across simulations were consistent with analytical standard errors for the C-index and D but not for the NRI. Good relative efficiency of the prediction metrics was observed in our examples, provided the sampling fraction was above 40% for the C-index, 60% for D, or 30% for the NRI. Stata code is made available.
  
  
  CONCLUSIONS
  Case-cohort designs can be used to provide unbiased estimates of the C-index, D measure and NRI.
authors.affiliation: Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Worts Causeway, Cambridge CB1 8RN, UK. lamp2@medschl.cam.ac.uk.
authors.forename: Jean
authors.initials: J
authors.lastname: Sanderson
authors.forename: Simon G
authors.initials: SG
authors.lastname: Thompson
authors.forename: Ian R
authors.initials: IR
authors.lastname: White
authors.forename: Thor
authors.initials: T
authors.lastname: Aspelund
authors.forename: Lisa
authors.initials: L
authors.lastname: Pennells
mesh_terms: D000465:Algorithms; D016022:Case-Control Studies; D003198:Computer Simulation; D003327:Coronary Disease / Q000453:epidemiology* / Q000209:etiology; D003627:Data Interpretation, Statistical; D006801:Humans; D015994:Incidence; D015233:Models, Statistical; D016016:Proportional Hazards Models; D018570:Risk Assessment; D012307:Risk Factors
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