Email updates

Keep up to date with the latest news and content from BMC Medical Research Methodology and BioMed Central.

Open Access Research article

Multiple imputation for estimating hazard ratios and predictive abilities in case-cohort surveys

Helena Marti1*, Laure Carcaillon2 and Michel Chavance1

Author Affiliations

1 Inserm, CESP Centre for Research in Epidemiology and Population Health, U1018, Biostatistics team, F-94807 Villejuif, France

2 Inserm, CESP Centre for Research in Epidemiology and Population Health, U1018, Hormones and Cardiovascular Disease team, F-94807 Villejuif, France

For all author emails, please log on.

BMC Medical Research Methodology 2012, 12:24  doi:10.1186/1471-2288-12-24

Published: 9 March 2012

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.