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Unformatted text preview: PUBH 7430 Lecture 14 J. Wolfson Division of Biostatistics University of Minnesota School of Public Health October 20, 2011 Fitting GEE models: Examples FEV data Mean model: E ( FEV ij  initheight i , age ij ) = β + β 1 initheight i + β 2 age ij • Focus on β 1 (influence of initial height, adjusted for age) • Fit Gamma family with identity link using: 1 “Naive” GLM 2 “Naive” GLM w/ sandwich variance estimates 3 GEE w/ independence working correlation matrix 4 GEE w/ other working correlations Fitting GEE models in R/SAS • Assume that data are in long format • Variable subjID identifies observations taken from the same cluster (girl) subjID init.height age fev 1 1.20 9.34 1.24 1 1.20 10.39 1.45 1 1.20 11.45 1.63 1 1.20 12.46 2.12 1 1.20 13.42 2.30 1 1.20 15.47 2.44 1 1.20 16.37 2.39 2 1.13 6.59 1.36 2 1.13 7.65 1.42 2 1.13 12.74 2.13 Fitting GEE models in R Use geeglm() function from geepack library: library(geepack) geeglm(fev~initheight+age, family=Gamma(link=’’identity’)’, id=subjID, corstr=’’) where can be independence , exchangeable , ar1 , unstructured , etc. Fitting GEE models in SAS Add REPEATED statement to PROC GENMOD : PROC GENMOD DATA=FEVDAT; MODEL FEV = INITHEIGHT AGE / DIST=GAMMA LINK=IDENTITY; REPEATED SUBJECT=SUBJID / TYPE=  where can be IND , EXCH , AR(1) , UN , etc. FEV example: “Naive” GLM In R glm(fev~initheight+age, family=Gamma(link=’’identity’’)) In SAS PROC GENMOD DATA=FEVDAT; MODEL FEV = INITHEIGHT AGE / DIST=GAMMA LINK=IDENTITY; FEV example: “Naive” GLM Variable Coef 95% Conf Int Intercept1.9 (2.11, 1.69) Initial Height (dm) 0.148 (0.13, 0.165) Age (years) 0.191 (0.186, 0.195) “Adjusting for age, two girls differing by 1 dm (10 cm) in initial height are expected to differ by 0.148 L (95% CI: 0.13 to 0.165 L) in FEV.” FEV example: “Naive” GLM For this analysis... • Estimate of initial height effect (0.148 L): Likely close to correct ( coefficient estimates are consistent ) • Confidence interval (width = 0.035 L): Likely incorrect ( sensitive to variance function and independence assumptions ) FEV: Naive GLM w/ sandwich variances • Coding “trick”: Use GEE code, but tell software that each observation is its own cluster (and use independence working correlation). • Define variable obsID : obsID subjID init.height age fev 1 1 1.20 9.34 1.24 2 1 1.20 10.39 1.45 3 1 1.20 11.45 1.63 4 1 1.20 12.46 2.12 5 1 1.20 13.42 2.30 6 1 1.20 15.47 2.44 7 1 1.20 16.37 2.39 8 2 1.13 6.59 1.36 9 2 1.13 7.65 1.42 10 2 1.13 12.74 2.13 FEV: Naive GLM w/ sandwich variances In R library(geepack) geeglm(fev~initheight+age,...
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This note was uploaded on 11/21/2011 for the course PUBH 7430 taught by Professor Prof.eberly during the Fall '04 term at Minnesota.
 Fall '04
 Prof.Eberly

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