Evaluation of a two‐part regression calibration to adjust for dietary exposure measurement error in the Cox proportional hazards model: A simulation study. Issue 4 (22nd March 2016)

• Record Type: Journal Article
• Authors: Agogo, George O.; van der Voet, Hilko; van't Veer, Pieter; van Eeuwijk, Fred A.; Boshuizen, Hendriek C.
• Is Part Of: Biometrical journal. Volume 58:Issue 4(2016:Jul.)
• Page Start: 766
• Page End: 782
• Publication Date: 2016-03-22
• Subjects: Attenuation -- Episodically consumed foods -- Measurement error -- Reference measurements -- Regression calibration; Biometry -- Periodicals; Medical statistics -- Periodicals; 570.15195
• DOI: 10.1002/bimj.201500009
• Languages: English
• ISSNs: 0323-3847

Abstract

Abstract : Dietary questionnaires are prone to measurement error, which bias the perceived association between dietary intake and risk of disease. Short‐term measurements are required to adjust for the bias in the association. For foods that are not consumed daily, the short‐term measurements are often characterized by excess zeroes. Via a simulation study, the performance of a two‐part calibration model that was developed for a single‐replicate study design was assessed by mimicking leafy vegetable intake reports from the multicenter European Prospective Investigation into Cancer and Nutrition (EPIC) study. In part I of the fitted two‐part calibration model, a logistic distribution was assumed; in part II, a gamma distribution was assumed. The model was assessed with respect to the magnitude of the correlation between the consumption probability and the consumed amount (hereafter, cross‐part correlation), the number and form of covariates in the calibration model, the percentage of zero response values, and the magnitude of the measurement error in the dietary intake. From the simulation study results, transforming the dietary variable in the regression calibration to an appropriate scale was found to be the most important factor for the model performance. Reducing the number of covariates in the model could be beneficial, but was not critical in large‐sample studies. The performance was remarkably robust when fitting a one‐part rather than a two‐part model. The model performance was minimally affected by the cross‐part correlation.