Quantile regression methods for reference growth charts

Ying Wei, Anneli Pere, Roger Koenker, Xuming He

Research output: Contribution to journalArticlepeer-review

Abstract

Estimation of reference growth curves for children's height and weight has traditionally relied on normal theory to construct families of quantile curves based on samples from the reference population. Age-specific parametric transformation has been used to significantly broaden the applicability of these normal theory methods. Non-parametric quantile regression methods offer a complementary strategy for estimating conditional quantile functions. We compare estimated reference curves for height using the penalized likelihood approach of Cole and Green (Statistics in Medicine 1992; 11:1305-1319) with quantile regression curves based on data used for modern Finnish reference charts. An advantage of the quantile regression approach is that it is relatively easy to incorporate prior growth and other covariates into the analysis of longitudinal growth data. Quantile specific autoregressive models for unequally spaced measurements are introduced and their application to diagnostic screening is illustrated.

Original languageEnglish (US)
Pages (from-to)1369-1382
Number of pages14
JournalStatistics in Medicine
Volume25
Issue number8
DOIs
StatePublished - Apr 30 2006

Keywords

  • Growth curves
  • Height
  • Longitudinal data
  • Quantile regression

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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