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Analysis of Growth Data
Prof. Paul Eilers, ing, PhD
Prof. Stef van Buuren, PhD, Prof. Paul Eilers, ing, PhD
Erasmus MC, Rotterdam
Courses for the Quantitative Researcher (SC17) or equivalent knowledge. Familiarity with basic statistical ideas. Experience with R is useful but not required.
Data on (human) growth are becoming available in increasing numbers and variety. Examples are height, body mass index (BMI), embryo size, and stages of pubertal development. Many advances in statistical techniques for analyzing such data have been made. Many researchers and practitioners working with this type of data are interested in description, statistical analysis, and the creation of reference curves.
The course will present modern methods for modelling the entire distribution (rather than just the mean) as a function of one or more covariates, typically age and sex. For continuous data, like weight and BMI, a flexible model is used to model distributions with changing location, spread, skewness and kurtosis. These aspects are assumed to change gradually with age, resulting in smooth curves that are estimated from the data. For discrete data, like stages of pubertal development, a multinomial model with smoothly changing class boundaries will be used. To explore data and diagnose model fit we use smooth quantile and expectile curves, Z-scores, worm plots, and Q statistics.
The course concentrates on cross-sectional data, and will describe the theory behind the proposed statistical tools. Excessive technical detail is avoided. Many graphical illustrations will be used so the course will appeal to a wide audience of epidemiologist, statisticians and (public) health professionals. Free software, written in the R language, is available and its use will be explained. The computer labs allow course participants to get hands-on experience, guided by the course presenters.
- to understand and apply modern methods for presenting and analyzing growth data.