Quantile regression methods for reference growth charts. Working

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 trans-formation has been used to significantly broaden the applicability of these normal theory methods. Nonparametric quantile regression methods offer a complementary strategy for estimating conditional quantile functions. We compare estimated ref-erence curves for height using the penalized likelihood approach of Cole and Green (1992) with quantile regression curves based on data used for modern Finnish refer-ence 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 longi-tudinal growth data. Quantile specific autoregressive models for unequally spaced measurements are introduced and their application to diagnostic screening is illus-trated. 1