Kristin Mühlenbruch1, Olga Kuxhaus1, Romina di Giuseppe2, Heiner Boeing3, Cornelia Weikert4, Matthias B Schulze5. 1. Department 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. 2. Research Group Cardiovascular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Postfach 65 21 33, Berlin 13316, Germany. 3. Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Arthur-Scheunert-Allee 114-116, 14558 Nuthetal, Germany. 4. Research Group Cardiovascular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Postfach 65 21 33, Berlin 13316, Germany; Department of Food Safety, Federal Institute of Risk Assessment, Max-Dohrn-Str. 8-10, Berlin 10589, Germany. 5. Department 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. Electronic address: mschulze@dife.de.
Abstract
OBJECTIVE: To compare weighting methods for Cox regression and multiple imputation (MI) in a case-cohort study in the context of risk prediction modeling. STUDY DESIGN AND SETTING: Based on the European Prospective Investigation into Cancer and Nutrition Potsdam study, we estimated risk scores to predict incident type-2 diabetes using full cohort data and case-cohort data assuming missing information on waist circumference outside the case-cohort (∼90%). Varying weighting approaches and MI were compared with regard to the calculation of relative risks, absolute risks, and predictive abilities including C-index, the net reclassification improvement, and calibration. RESULTS: The full cohort comprised 21,845 participants, and the case-cohort comprised 2,703 participants. Relative risks were similar across all methods and compatible with full cohort estimates. Absolute risk estimates showed stronger disagreement mainly for Prentice and Self & Prentice weighting. Barlow and Langholz & Jiao weighting methods and MI were in good agreement with full cohort analysis. Predictive abilities were closest to full cohort estimates for MI or for Barlow and Langholz & Jiao weighting. CONCLUSIONS: MI seems to be a valid method for deriving or extending a risk prediction model from case-cohort data and might be superior for absolute risk calculation when compared to weighted approaches.
OBJECTIVE: To compare weighting methods for Cox regression and multiple imputation (MI) in a case-cohort study in the context of risk prediction modeling. STUDY DESIGN AND SETTING: Based on the European Prospective Investigation into Cancer and Nutrition Potsdam study, we estimated risk scores to predict incident type-2 diabetes using full cohort data and case-cohort data assuming missing information on waist circumference outside the case-cohort (∼90%). Varying weighting approaches and MI were compared with regard to the calculation of relative risks, absolute risks, and predictive abilities including C-index, the net reclassification improvement, and calibration. RESULTS: The full cohort comprised 21,845 participants, and the case-cohort comprised 2,703 participants. Relative risks were similar across all methods and compatible with full cohort estimates. Absolute risk estimates showed stronger disagreement mainly for Prentice and Self & Prentice weighting. Barlow and Langholz & Jiao weighting methods and MI were in good agreement with full cohort analysis. Predictive abilities were closest to full cohort estimates for MI or for Barlow and Langholz & Jiao weighting. CONCLUSIONS: MI seems to be a valid method for deriving or extending a risk prediction model from case-cohort data and might be superior for absolute risk calculation when compared to weighted approaches.
Authors: Ina Danquah; Juliet Addo; Daniel Boateng; Kerstin Klipstein-Grobusch; Karlijn Meeks; Cecilia Galbete; Erik Beune; Silver Bahendeka; Joachim Spranger; Frank P Mockenhaupt; Karien Stronks; Charles Agyemang; Matthias B Schulze; Liam Smeeth Journal: Sci Rep Date: 2019-07-26 Impact factor: 4.379