Abstract
BackgroundThe 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).MethodsWe 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.ResultsWhen 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.ConclusionsMI 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.
Highlights
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
multiple imputation (MI) estimates of net reclassification improvement (NRI) and integrated discrimination index (IDI) indexes were close to those obtained with the full cohort analysis and did not depend on the subcohort size
When the effect of the phase-2 variable was not null, the rejection percentage of the null hypothesis IDI = 0 was similar with MI and with full cohort analysis
Summary
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. In case-cohort studies, the non-cases who do not belong to the subcohort are incompletely observed by design, enabling cost reduction with a small loss of efficiency. Various approaches have been described to estimate the proportional hazard model in case-cohort surveys: Weighted estimators [2,3,4,5,6] are classically used in these surveys, with analysis restricted to the completely observed subsample, so the information collected for variables. The former was slightly more precise than the latter for the phase-2 variable. In practise, the distribution of the phase-2 variable is unknown and onemay wonder how MI compares to weighted estimators when the imputation model is misspecified
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