Nutritional epidemiology methods and related statistical challenges and opportunities

ABSTRACT

The public health importance of nutritional epidemiology research is discussed, along with methodological challenges to obtaining reliable information on dietary approaches to chronic disease prevention. Measurement issues in assessing dietary intake need to be addressed to obtain reliable disease association information. Self-reported dietary data typically incorporate major random and systematic biases. Intake biomarkers offer potential for more reliable analyses, but biomarkers have been established only for a few dietary variables, and these may be too expensive to apply to all participants in large epidemiologic cohorts. A possible way forward involves additional nutritional biomarker development using high-dimensional metabolomic profiling, using blood and urine specimens, in conjunction with further development of statistical approaches for accommodating measurement error with failure time response data. Statisticians have the opportunity to contribute greatly to worldwide public health through the development of statistical methods to address these nutritional epidemiology research challenges, as is elaborated in this contribution.

KEYWORDS:

• Chronic disease
• epidemiology
• failure time data
• hazard ratio
• measurement error
• metabolomics
• nutritional biomarker
• regression calibration

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This manuscript was written with partial support from National Institutes of Health grants R01 CA210921, R01 CA119171 and P30 CA015704.

Notes on contributors

Ross L. Prentice

Ross L. Prentice is a Member and Former Director of the Public Health Sciences Division of the Fred Hutchinson Cancer Research Center and is Professor of Biostatistics at the University of Washington.

Ying Huang

Ying Huang is Associate Member in Biostatistics at the Public Health Sciences and Vaccine & Infectious Diseases Divisions of the Fred Hutchinson Cancer Research Center, and Affiliate Associate Professor in the Department of Biostatistics at the University of Washington.