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Multiple imputation was a valid approach to estimate absolute risk from a prediction model based on case-cohort data
Kristin Mühlenbruch1, Olga Kuxhaus1, Romina di Giuseppe2
1Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Arthur-Scheunert-Allee 114-116, 14558 Nuthetal, Germany; German Center for Diabetes Research (DZD), Ingolstädter Landstr. 1, Neuherberg 85764, Germany.
Journal of Clinical Epidemiology
|February 1, 2017
Summary
Multiple imputation (MI) and specific weighting methods accurately predict disease risk using case-cohort data. MI shows superiority for absolute risk calculations in risk prediction modeling.
Area of Science:
- Epidemiology
- Biostatistics
- Risk Prediction Modeling
Background:
- Case-cohort studies are efficient for epidemiological research but require careful statistical handling of data.
- Risk prediction models are crucial for identifying individuals at high risk of diseases like type-2 diabetes.
- Missing data is a common challenge in large cohort studies, impacting model accuracy.
Purpose of the Study:
- To compare the performance of different weighting methods and multiple imputation (MI) for Cox regression in case-cohort studies.
- To evaluate these methods in the context of risk prediction modeling for type-2 diabetes.
- To assess their accuracy in estimating relative risks, absolute risks, and predictive abilities.
Main Methods:
- Utilized data from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study.
- Estimated type-2 diabetes risk scores using full cohort and case-cohort data with substantial missingness (∼90%) for waist circumference.
- Compared various weighting approaches (Prentice, Self & Prentice, Barlow, Langholz & Jiao) and MI against full cohort estimates.
Main Results:
- Relative risk estimates were consistent across all compared methods.
- Absolute risk estimates showed discrepancies, particularly with Prentice and Self & Prentice weighting.
- Barlow, Langholz & Jiao weighting, and MI demonstrated good agreement with full cohort analyses for absolute risks.
- Multiple imputation (MI) and Barlow/Langholz & Jiao weighting yielded predictive abilities closest to full cohort estimates.
Conclusions:
- Multiple imputation (MI) is a viable and potentially superior method for developing or extending risk prediction models using case-cohort data.
- MI may offer advantages over weighted approaches, especially for accurate absolute risk estimation.
- The findings support the use of MI in handling missing data within case-cohort designs for robust risk prediction.
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