Application of Nonparametric Quantile Regression for Fitting Height-for-age Curves
Jundishapur Journal of Health Sciences: 6 (1); 221-226
January 9, 2014
Article Type: Research Article
M T, Safarian
H. Application of Nonparametric Quantile Regression for Fitting Height-for-age Curves,
Jundishapur J Health Sci.
Online ahead of Print
Reference curves are useful tools to monitor childrens growth status and can promote growth velocity in infants. In this regard, various parametric and semi-parametric methods are frequently used in the last decades. In the present paper, nonparametric quintile regression method is used as a powerful and applicable methodology to estimate height curves and normal values of height-for-age in children aged 0 to 5 years. The results of this study are compared with World Health Organization (WHO) references and semi-parametric LMS method of Cole and Green.
As part of a national survey, 70,737 apparently healthy boys and girls aged 0 to 5 years were recruited in July 2004 for 20 days from among those referring to the community clinics for routine health check-ups. Anthropometric measurements were conducted by trained health staff using WHO methodology. To estimate curves and normal values, we applied the nonparametric quintile regression method obtained by local constant kernel estimation of conditional quintile curves.
Studying a population of boys and girls aged 0 to 5 years living in the northeast of Iran, the weight-for-age growth curves were derived. The results were consistent to those obtained by a semi-parametric
LMS method with the same data. The median values of the childrens weight in all the age groups were lower than the corresponding values in WHO reference data. The weight curves of boys were higher than those of girls in all age groups.
The differences between growth patterns of children living in the northeast of Iran versus the international ones are considerable which necessitate applying local and regional growth charts. International normal values may not properly recognize the populations at risk for growth problems in the Iranian children. Quintile regression (QR) which does not require restricted assumptions is a flexible method, which is proposed for estimating reference curves and normal values.
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