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Quantile regression methods for reference growth charts.

Ying Wei1, Anneli Pere, Roger Koenker

  • 1Department of Biostatistics, Columbia University, New York, NY, USA.

Statistics in Medicine
|September 7, 2005
PubMed
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This study compares traditional methods with quantile regression for estimating children's growth curves. Quantile regression offers a flexible approach for analyzing longitudinal growth data and incorporating covariates.

Area of Science:

  • Pediatrics
  • Biostatistics
  • Growth Monitoring

Background:

  • Traditional methods for estimating children's growth curves rely on normal theory.
  • Parametric transformations have expanded the applicability of normal theory methods.
  • Non-parametric quantile regression provides an alternative for estimating conditional quantile functions.

Purpose of the Study:

  • To compare height reference curves estimated by penalized likelihood (Cole and Green) with those from quantile regression.
  • To evaluate the utility of quantile regression for analyzing longitudinal growth data.
  • To introduce and illustrate quantile-specific autoregressive models for diagnostic screening.

Main Methods:

  • Comparison of penalized likelihood approach with quantile regression.

Related Experiment Videos

  • Application of quantile regression to modern Finnish reference chart data.
  • Development of quantile-specific autoregressive models for unequally spaced measurements.

    • Main Results:

    • Quantile regression provides a complementary strategy to normal theory methods.
    • Quantile regression facilitates the incorporation of prior growth and covariates.
    • Quantile-specific autoregressive models demonstrate utility in diagnostic screening.

    Conclusions:

    • Quantile regression offers an advantageous approach for estimating children's growth curves.
    • The method is flexible and can incorporate complex growth data.
    • The introduced models show promise for diagnostic applications in growth monitoring.