Growth Charts of Body Mass Index (BMI) with
SAS Institute Inc. Cary, NC, U.S.A. ∗
Growth charts of body mass index (BMI) are constructed from the recent four-year national cross-
sectional survey data (1999−2002) using parametric quantile regression methods, which are implemented
with a newly developed SAS procedure (http://www.sas.com/statistics) and SAS macros.
KEY WORDS: Body mass index, growth charts, quantile regression, smoothing algorithm, simplex, interior
Overweight has become a common problem in public health, especially for children. Obesity has been
related to numerous health risks, both physical and psychological. Body mass index, defined as the ratio of
weight (kg) to squared height (m2), has been popularly used as a measure of overweight and obesity.
The percentiles of BMI for a specified age is of particular interest in light of public health concerns.
Not only are the upper percentiles closely watched for overweight and obesity, the lower percentiles are also
observed for underweight. The empirical percentiles with grouped age provide a discrete approximation for
the population percentiles. However, continuous percentile curves are both more accurate and attractive.
There have been several methods used to construct such age-dependent growth charts. Early methods
fit smoothing curves on sample quantiles of segmented age groups. However, such methods are not robust to
outliers. Large sample size is needed in order to estimate the percentiles in each age group with appropriate
precision. The segmentation may lose information from nearby groups. To avoid segmentation, Cole and
Green (1992) developed a Box-Cox transformation-based semiparametric approach from the LMS (Lamda-
Mu-Sigma) method introduced by Cole (1988). The semiparametric LMS method solves penalized likelihood
equations. Because of the lack of finite expectation for some of the derivatives of the penalized log-likelihood,
solutions of these equations could be sensitive to a s