A Hierarchical Regression Model for Dietary Data Adjusting for Covariates Measurement Error by Regression Calibration: An Application to a Large Prospective Study for Diabetic Complications

Abstract

To examine the effect of food intakes on the occurrence of a specific disease, it is necessary to take account of numerous measurement errors in dietary assessment instruments, such as the 24-hour recall or the food frequency questionnaire. The regression calibration (RC) method has been widely used for correcting the measurement error. However, the resulting corrected estimator is generally more variable than the naive biased one. Using the Bayesian hierarchical regression models, one can obtain more precise estimates than using ordinary regression models by incorporating additional information into a second-stage regression. In this paper, we propose a hierarchical Poisson regression model, in which multivariate measurement errors are adjusted by RC method. Simulation studies were conducted to investigate the performances of the proposed method, which showed that the proposed estimators were nearly unbiased, and were more precise than the usual RC ones even in the case of a few number of exposure. We also applied the proposed method to the analysis of a large prospective study, JDCS (Japan Diabetes Complications Study), to examine the effect of food group intakes on the occurrence of the cardiovascular disease (CVD) among type2 diabetic patients.