Preface to First Edition - lib

ANALYSIS USING R: GEE 239R> summary(btb_gee)...Model:Link:IdentityVariance to Mean Relation: GaussianCorrelation Structure: Independent...Coefficients:Estimate Naive S.E. Naive z Robust S.E. Robust z(Intercept) 3.569 1.4833 2.41 2.2695 1.572bdi.pre 0.582 0.0564 10.32 0.0916 6.355trtBtheB -3.237 1.1296 -2.87 1.7746 -1.824length>6m 1.458 1.1380 1.28 1.4826 0.983drugYes -3.741 1.1766 -3.18 1.7827 -2.099Estimated Scale Parameter: 79.3...Figure 13.2R output of the summary method for the btb_gee model (slightlyabbreviated).GEE procedure is 0.676, very similar to the estimated intra-class correlationcoefficient from the random intercept model. i.e., 7.03 2 /(5.07 2 + 7.03 2 ) = 0.66– see Figure 12.2.13.3.2 Respiratory IllnessWe will now apply the GEE procedure to the respiratory data shown inTable 13.1. Given the binary nature of the response variable we will choosea binomial error distribution and by default a logistic link function. We shallalso fix the scale parameter φ described in Chapter 7 at one. (The defaultin the gee function is to estimate this parameter.) Again we will apply theprocedure twice, firstly with an independence structure and then with anexchangeable structure for the working correlation matrix. We will also fit alogistic regression model to the data using glm so we can compare results.The baseline status, i.e., the status for month == 0, will enter the modelsas an explanatory variable and thus we have to rearrange the data.framerespiratory in order to create a new variable baseline:R> data("respiratory", package = "HSAUR2")R> resp "0")R> resp$baseline