Regression calibration utilizing biomarkers developed from high-dimensional metabolites.
Front Nutr
; 10: 1215768, 2023.
Article
en En
| MEDLINE
| ID: mdl-37599686
ABSTRACT
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.
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1
Bases de datos:
MEDLINE
Tipo de estudio:
Prognostic_studies
Idioma:
En
Revista:
Front Nutr
Año:
2023
Tipo del documento:
Article
País de afiliación:
Estados Unidos