The use of nonparametric quantile regression and least median of squares regression for construction of growth curves of weight

Abstract

Objective: This study aimed to investigate the use of the Least Median Squares (LMS) regression and nonparametric quantile regression model comparatively to describe children?s weight growth. Material and Methods: Two different models were used to obtain the percentile curves to identify the weight growth in girls. The first model was obtained by LMS regression, which is a member of the family of nonlinear parametric quantile regression. In addition, in this model percentile curves used to define growth were generated using the Box-Cox transformation and the cubic spline. The second model was obtained by nonparametric quantile regression that did not require the assumption of a normal distribution for construction of percentile curves. This method is a flexible approach, as well as being computationally simple. The weight values obtained from 1771 healthy girls aged between 6 and 14 years were used in both methods. The data were collected from the cross-sectional study conducted in schools in Düzce city. Results: The distributions of weight measurements according to ages revealed that there were deviations from normality at some ages, there were deviated values in the tail regions of the distribution, and the variances changed according to ages. Using both methods, growth curves were constructed separately for each age group. Predicted values of the LMS and the non-parametric quantile regression models were similar for each age. In addition, the error sum of squares derived from non-parametric quantile regression was lower than that derived from LMS regression for each percentile curve. Moreover, the estimations obtained from both methods were highly correlated with the estimation values of the province İstanbul, which was considered the reference. Conclusion: When the assumptions about the distribution and variances of the data are violated and these assumptions cannot be achieved with the transformation, nonparametric quantile regression method gives more reliable results for the creation of percentile curves. © 2013 by Türkiye Klinikleri.