Back to Annual Meeting Page		American Public Health Association 133rd Annual Meeting & Exposition December 10-14, 2005 Philadelphia, PA

Abstract #122174

John McGready, MA¹, J.E. Hoover-Fong², K.J. Schulze³, H. Barnes², and C.I. Scott⁴. (1) Department of Biostatistics, Johns Hopkins University, Bloomberg School of Public Health, 615 N. Wolfe St., Baltimore, MD 21205, 410-955-5000, jmcgread@jhsph.edu, (2) Greenberg Center for Skeletal Dysplasias, McKusick-Nathans Institute of Genetic Medicine, 99999, Baltimore, MD 20000, (3) Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe St., Baltimore, MD 21205, (4) AI DuPont Hospital for Children, 99999, Wilmington, DE 00000

Achondroplasia (dwarfism) is the most common skeletal dysplasia, occurring in roughly 1 in 10,000 births. Subjects with the condtion are similar to subjects without the condition (generally refered to as "averages") in height and weight at birth, but grow at a slower rate as they age. Accurate assessment of growth parameters in skeletal dysplasias patients is problematic with existing growth curves. Most were constructed from a small number of patients with a dearth of longitudinal data. Furthermore, data were compiled from multiple clinical settings using potentially non-standardized observational methods, and the curves were derived from very basic parametric analysis. Of clinical significance, weight-for-age norms are currently unavailable. My collaborators have collected extensive, longitudinal anthropometric data from medical records of patients with achondroplasia (n=334), with >2000 datapoints for height, weight and head circumference.

Using the R freeware package, percentiles (5, 25, 50, 75, and 95th) were estimated across the age continuum for each growth parameter using moving 1 month and 6 month windows. Percentiles were then smoothed using a quadratic, penalized smoother. Errors in the resulting curves were estimated both via boostrapping, and Bayesian semi-parametric techniques. While this may seem like a pretty straightforward analysis, it was actually ripe with some computational challenges for this R-novice. After consulting with colleagues with expertise in R, and web based clearing houses for R macros and information, I realized that this was not a trivial analysis.

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