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Assessment of Child Anthropometry in a Large Epidemiologic Study
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Composite marginal quantile regression analysis for longitudinal adolescent body mass index data.

Chi-Chuan Yang1, Yi-Hau Chen1, Hsing-Yi Chang2

  • 1Institute of Statistical Science, Academia Sinica, Taipei, 11529, Taiwan.

Statistics in Medicine
|June 3, 2017
PubMed
Summary

Childhood obesity is linked to adult health issues. A new statistical method analyzes longitudinal adolescent body mass index (BMI) data, considering individual, family, and school factors for better insights.

Keywords:
clustered datageneralized estimating equationquantile regression coefficients modeling

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Area of Science:

  • Biostatistics
  • Public Health
  • Pediatrics

Background:

  • Childhood and adolescent overweight/obesity, measured by body mass index (BMI), predicts adult obesity and related health conditions.
  • Understanding factors influencing BMI trajectories during adolescence is crucial for early intervention.

Purpose of the Study:

  • To develop and evaluate a novel statistical method for analyzing longitudinal adolescent BMI data.
  • To identify individual, family, and school factors associated with marginal quantiles of adolescent BMI.

Main Methods:

  • Proposed a composite marginal quantile regression analysis for longitudinal data.
  • Extended existing quantile regression methods to simultaneously analyze multiple quantile levels.
  • Developed a goodness-of-fit test for the proposed model.
  • Utilized data from the Child and Adolescent Behaviors in Long-term Evolution (CABLE) study.

Main Results:

  • The proposed method demonstrated higher efficiency compared to analyses ignoring data correlation or performing separate quantile regressions.
  • The method effectively accounts for the correlation structure inherent in longitudinal observations.
  • The application to CABLE study data confirmed the practical utility of the proposed approach.

Conclusions:

  • The novel composite marginal quantile regression method provides an efficient tool for analyzing longitudinal adolescent BMI.
  • This approach offers a more comprehensive understanding of factors influencing BMI across different levels in adolescents.
  • Accurate analysis of adolescent BMI is vital for addressing the public health challenge of obesity.