Preface to First Edition - lib

ANALYSIS USING R: GEE 245R> layout(matrix(1:2, nrow = 1))R> ylim boxplot(log(seizure.rate + 1) ~ period, data = placebo,+ main = "Placebo", ylab = "Log number of seizures",+ xlab = "Period", ylim = ylim)R> boxplot(log(seizure.rate + 1) ~ period, data = progabide,+ main = "Progabide", ylab = "Log number of seizures",+ xlab = "Period", ylim = ylim)PlaceboProgabideLog number of seizures0 1 2 3 4Log number of seizures0 1 2 3 41 2 3 4Period1 2 3 4PeriodFigure 13.8Boxplots of log of numbers of seizures in each two-week period postrandomisation for placebo and active treatments.of explanatory variables). In our example the observation period is two weeks,so we simply need to set log(2) for each observation as the offset.We can now fit a Poisson regression model to the data assuming independenceusing the glm function. We also use the GEE approach to fit an independencestructure, followed by an exchangeable structure using the followingR code:R> per epilepsy$period names(epilepsy)[names(epilepsy) == "treatment"] fm epilepsy_glm epilepsy_gee1