Using Controlled Feeding Study for Biomarker Development in Regression Calibration for Disease Association Estimation

Cheng Zheng (), Yiwen Zhang, Ying Huang and Ross Prentice
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Cheng Zheng: University of Nebraska Medical Center
Yiwen Zhang: University of Wisconsin-Milwaukee
Ying Huang: Fred Hutchinson Cancer Research Center
Ross Prentice: Fred Hutchinson Cancer Research Center

Statistics in Biosciences, 2023, vol. 15, issue 1, No 3, 57-113

Abstract: Abstract Correction for systematic measurement error in self-reported data is an important challenge in association studies of dietary intake and chronic disease risk. The regression calibration method has been used for this purpose when an objectively measured biomarker is available. However, a big limitation of the regression calibration method is that biomarkers have only been developed for a few dietary components. We propose new methods to use controlled feeding studies to develop valid biomarkers for many more dietary components and to estimate the diet disease associations. Asymptotic distribution theory for the proposed estimators is derived. Extensive simulation is performed to study the finite sample performance of the proposed estimators. We applied our method to examine the associations between the sodium/potassium intake ratio and cardiovascular disease incidence using the Women’s Health Initiative cohort data. We discovered positive associations between sodium/potassium ratio and the risks of coronary heart disease, nonfatal myocardial infarction, coronary death, ischemic stroke, and total cardiovascular disease.

Keywords: Measurement error; Regression calibration; Feeding study; Biomarker; Cardiovascular disease (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s12561-022-09349-3

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