Regression calibration utilizing biomarkers developed from high-dimensional metabolites

Frontiers in Nutrition

Nutrition

Ran Dai¹  Cheng Zheng¹  Ying Huang²  Ross L. Prentice²  Yiwen Zhang³ 

[1] Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE, United States;Public Health Science Division, Fred Hutchinson Cancer Center, Seattle, WA, United States;Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, United States;

关键词: measurement error;    regression calibration;    feeding study;    biomarker;    high-dimensional data;   

DOI  :  10.3389/fnut.2023.1215768

 received in 2023-05-02, accepted in 2023-07-17,  发布年份 2023

来源: Frontiers

【 摘 要 】

Addressing systematic measurement errors in self-reported data is a critical challenge in association studies of dietary intake and chronic disease risk. The regression calibration method has been utilized for error correction when an objectively measured biomarker is available; however, biomarkers for only a few dietary components have been developed. This paper proposes to use high-dimensional objective measurements to construct biomarkers for many more dietary components and to estimate the diet disease associations. It also discusses the challenges in variance estimation in high-dimensional regression methods and presents a variety of techniques to address this issue, including cross-validation, degrees-of-freedom corrected estimators, and refitted cross-validation (RCV). Extensive simulation is performed to study the finite sample performance of the proposed estimators. The proposed method is applied to the Women's Health Initiative cohort data to examine the associations between the sodium/potassium intake ratio and the total cardiovascular disease.

【 授权许可】

Unknown   
Copyright © 2023 Zhang, Dai, Huang, Prentice and Zheng.