%0 Journal Article %T Multiple imputation for estimating hazard ratios and predictive abilities in case-cohort surveys. %+ Centre de recherche en épidémiologie et santé des populations (CESP) %A Marti, Helena %A Carcaillon, Laure %A Chavance, Michel %Z This study was supported by a grant from the Région Île-de-France. It used data from the Three-City study which is conducted under an agreement between the Institut National de la Santé et de la Recherche Médicale and the Université Victor Segalen-Bordeaux 2 %< avec comité de lecture %@ 1471-2288 %J BMC Medical Research Methodology %I BioMed Central %V 12 %N 1 %P 24 %8 2012-03-09 %D 2012 %R 10.1186/1471-2288-12-24 %M 22405090 %Z Life Sciences [q-bio]/Santé publique et épidémiologieJournal articles %X 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. %G English %2 https://inserm.hal.science/inserm-00710028/document %2 https://inserm.hal.science/inserm-00710028/file/1471-2288-12-24.pdf %L inserm-00710028 %U https://inserm.hal.science/inserm-00710028 %~ INSERM %~ UNIV-PSUD %~ APHP %~ SANTE_PUB_INSERM %~ CESP %~ UVSQ %~ 3CITS %~ GS-SANTE-PUBLIQUE