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Quantile regression methods for reference growth charts
Author(s) -
Wei Ying,
Pere Anneli,
Koenker Roger,
He Xuming
Publication year - 2006
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2271
Subject(s) - statistics , quantile regression , cross sectional regression , econometrics , regression , computer science , regression analysis , mathematics , polynomial regression
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. Copyright © 2005 John Wiley & Sons, Ltd.