Selecting the Working Correlation Structure in Generalized Estimating Equations with Application to the Lung Health Study

The generalized estimating equation GEE approach is becoming more and more popular in handling correlated response data for example in longitudinal studies An attractive property of the GEE is that one can use some working correlation structure that may be wrong but the resulting regression coe cient estimate is still consistent and asymptotically normal One convenient choice is the independence model treat ing the correlated responses as if they were independent However with time varying covariates there is a dilemma using the independence model may be very ine cient Fitzmaurice using a non diagonal working correlation matrix may violate an important assumption in GEE producing biased estimates Pepe and Anderson It would be desirable to be able to distinguish these two situations based on the data at hand More generally selecting an appropriate working correlation structure as an aspect of model selection may improve estimation e ciency In this paper we propose some resampling based methods i e the bootstrap and cross validation to do it The methodology is demonstrated by application to the Lung Health Study LHS data to investigate the e ects of smoking cessation on lung function and on the symptom of chronic cough In addition Pepe and Anderson s result is veri ed using the LHS data