Abstract
Multilevel modeling is a flexible approach for the analysis of nested data structures, such as those encountered in longitudinal studies with repeated measures of an outcome of interest taken across time and nested within subjects. The baseline score on the outcome and rate of change vary across subjects, and subject level predictor variables may be used to explain part of the between-subject variability. This contribution shows how to formulate linear and logistic models for continuous and binary outcomes. A study of the effect of growth hormone in adolescents with short stature is used as an illustrative example to demonstrate the use of these models and to aid in the interpretation of model parameter estimates. Attention is also paid to sufficient sample sizes, and two methods to explore the relation between sample size and power of statistical tests are discussed.
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