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Multiple imputation for estimating hazard ratios and predictive abilities in case-cohort surveys
Helena Marti1, Laure Carcaillon, Michel Chavance
1Inserm, CESP Centre for Research in Epidemiology and Population Health, U1018, Biostatistics team, F-94807 Villejuif, France. helena.marti-soler@inserm.fr
BMC Medical Research Methodology
|March 13, 2012
Summary
Multiple imputation (MI) offers an efficient method for analyzing case-cohort studies and assessing model predictive ability. This approach provides accurate estimates when the imputation model is correct, simplifying complex data analysis.
Area of Science:
- Epidemiology
- Biostatistics
- Health Research Methodology
Background:
- Traditional weighted estimators for case-cohort studies lack efficiency.
- Model predictive ability estimates from case-cohort data are sensitive to subcohort size.
- Case-cohort studies present unique incomplete data challenges, necessitating advanced analytical methods like multiple imputation (MI).
Purpose of the Study:
- To validate the use of multiple imputation (MI) for analyzing case-cohort data.
- To assess the accuracy of MI in estimating hazard ratios and model predictive abilities.
- To evaluate the predictive ability of D-dimer plasma concentration for coronary heart disease (CHD) and vascular dementia (VaD) in a case-cohort study.
Main Methods:
- Conducted simulation studies to test the MI approach for case-cohort data.
- Applied MI to a real-world case-cohort survey from the Three-City study.
- Compared MI estimates with full data estimates and assessed predictive performance.
Main Results:
- MI estimates for hazard ratios and predictive abilities closely matched full data results when the imputation model was correctly specified.
- Misspecified imputation models led to biased estimates of hazard ratios and predictive abilities.
- In the Three-City study, elevated D-dimer levels were associated with increased VaD risk (HR=1.69), but did not enhance model predictive ability.
Conclusions:
- Multiple imputation (MI) provides a straightforward and effective method for analyzing case-cohort data.
- MI facilitates the evaluation of a model's predictive ability or the contribution of additional variables.
- The MI approach simplifies the analysis of complex epidemiological data from case-cohort designs.
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