Development of a pediatric body mass index using longitudinal single-index models

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Assessment of Child Anthropometry in a Large Epidemiologic Study
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Assessment of Child Anthropometry in a Large Epidemiologic Study

Published on: February 2, 2017

Jingwei Wu1, Wanzhu Tu2

  • 1Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN 46202, USA wujw@iupui.edu.

Statistical Methods in Medical Research
|January 11, 2013
PubMed

Insights

This study introduces a novel pediatric body mass index (BMI) for better health predictions in children. The new measure, derived using advanced statistical models, shows superior performance compared to the traditional BMI.

Area of Science:

  • Biostatistics
  • Pediatric Health
  • Epidemiology

Background:

  • The standard body mass index (BMI), calculated as weight/height(2), is widely used for assessing human adiposity.
  • The suitability of BMI for accurately quantifying pediatric body mass and predicting health outcomes during puberty remains understudied.
  • There is a need for improved pediatric body mass measures to better assess health risks.

Purpose of the Study:

  • To propose and validate an alternative pediatric body mass measure for predicting blood pressure in children.
  • To develop and implement advanced statistical modeling techniques for creating this new metric.
  • To evaluate the performance of the proposed measure against the traditional body mass index.

Main Methods:

  • Utilized partially linear single-index mixed effect models to determine a new pediatric body mass metric.
  • Developed an efficient algorithm for fitting these models in longitudinal data scenarios.
  • Conducted simulations to compare the proposed method with the standard body mass index.

Main Results:

  • The proposed single-index modeling technique offers a superior method for predicting blood pressure in children compared to standard BMI.
  • The developed algorithm for fitting partially linear single-index models is efficient and implementable on common computational platforms.
  • Simulations demonstrated the enhanced predictive performance of the new pediatric body mass measure.

Conclusions:

  • The novel pediatric body mass measure shows significant potential for improving health outcome predictions in children.
  • The developed statistical methodology is robust and applicable to various longitudinal data analyses.
  • This approach can be extended to create other specialized indices for specific health investigations in pediatric populations.

Keywords:
Adiposity measurementbody mass indexcubic splinemixed effect modelpenalized nonlinear least squarerepeated measures

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