title: Multiple imputation for estimating hazard ratios and predictive abilities in case-cohort surveys.
abstract: BACKGROUND
  The weighted estimators generally used for analyzing case-cohort studies are not fully efficient and naive estimates of the predictive ability of a model from case-cohort data depend on the subcohort size. However, case-cohort studies represent a special type of incomplete data, and methods for analyzing incomplete data should be appropriate, in particular multiple imputation (MI).
  
  
  METHODS
  We performed simulations to validate the MI approach for estimating hazard ratios and the predictive ability of a model or of an additional variable in case-cohort surveys. As an illustration, we analyzed a case-cohort survey from the Three-City study to estimate the predictive ability of D-dimer plasma concentration on coronary heart disease (CHD) and on vascular dementia (VaD) risks.
  
  
  RESULTS
  When the imputation model of the phase-2 variable was correctly specified, MI estimates of hazard ratios and predictive abilities were similar to those obtained with full data. When the imputation model was misspecified, MI could provide biased estimates of hazard ratios and predictive abilities. In the Three-City case-cohort study, elevated D-dimer levels increased the risk of VaD (hazard ratio for two consecutive tertiles = 1.69, 95%CI: 1.63-1.74). However, D-dimer levels did not improve the predictive ability of the model.
  
  
  CONCLUSIONS
  MI is a simple approach for analyzing case-cohort data and provides an easy evaluation of the predictive ability of a model or of an additional variable.
authors.affiliation: Inserm, CESP Centre for Research in Epidemiology and Population Health, U1018, Biostatistics team, F-94807 Villejuif, France. helena.marti-soler@inserm.fr
authors.forename: Helena
authors.initials: H
authors.lastname: Marti
authors.forename: Laure
authors.initials: L
authors.lastname: Carcaillon
authors.forename: Michel
authors.initials: M
authors.lastname: Chavance
mesh_terms: D000368:Aged; D015415:Biomarkers / Q000032:analysis; D001777:Blood Coagulation / Q000502:physiology; D015331:Cohort Studies; D003198:Computer Simulation; D003327:Coronary Disease / Q000453:epidemiology / Q000209:etiology; D003627:Data Interpretation, Statistical; D015140:Dementia, Vascular / Q000453:epidemiology / Q000209:etiology; D005260:Female; D005342:Fibrinolysis / Q000502:physiology; D005602:France; D006801:Humans; D008297:Male; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D015203:Reproducibility of Results; D012111:Residence Characteristics; D018570:Risk Assessment / Q000379:methods*; D015983:Selection Bias; D012923:Social Class; D016019:Survival Analysis
chemical_list: D015415:Biomarkers