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Assessment of Child Anthropometry in a Large Epidemiologic Study

Assessment of Child Anthropometry in a Large Epidemiologic Study

Published on: February 2, 2017

Measurement error modeling and nutritional epidemiology association analyses.

Ross L Prentice¹, Ying Huang

¹Fred Hutchinson Cancer Research Center, 1100 Fairview Avenue North, Seattle, Washington, USA 98109.

The Canadian Journal of Statistics = Revue Canadienne De Statistique

|April 23, 2013

Summary

This study on nutritional epidemiology highlights measurement error in assessing diet and disease risk. Advanced statistical methods, including modeling body mass index, are crucial for accurate findings in cancer and cardiovascular disease research.

Keywords:

cancer cardiovascular disease diet epidemiology failure time data measurement error

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Area of Science:

Nutritional Epidemiology
Biomarker Research
Chronic Disease Risk Factors

Background:

The Women's Health Initiative Nutrient Biomarker Study provides a foundation for dietary intake research.
Accurate assessment of energy and protein consumption is vital for understanding chronic disease etiology.
Measurement error in dietary assessment poses a significant challenge in nutritional epidemiology.

Purpose of the Study:

To apply findings from the Nutrient Biomarker Study to analyze associations between diet and major cancers/cardiovascular diseases.
To emphasize the importance of measurement error modeling in nutritional epidemiology.
To address the complex role of body mass index (BMI) as a potential mediator or confounder in disease association analyses.

Main Methods:

Utilized measurement error modeling and advanced data analysis techniques.
Developed and applied a hazard ratio parameter estimation procedure accounting for BMI as a mediating variable.
Integrated BMI assessment within energy and protein consumption evaluations.

Main Results:

Demonstrated the critical role of addressing measurement issues for progress in nutritional epidemiology.
Provided a method for hazard ratio estimation that incorporates BMI's mediating effects.
Highlighted the challenges and value of modeling BMI in disease association studies.

Conclusions:

Accurate dietary assessment, particularly concerning energy and protein, is fundamental for reliable nutritional epidemiology.
Statistical methodologies addressing measurement error and mediating variables like BMI are essential for robust findings.
Future research includes developing biomarkers for additional dietary components through human feeding studies.