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GEE estimation of a misspecified time‐varying covariate: an example with the effect of alcoholism treatment on medical utilization
Author(s) -
Wall Melanie M.,
Dai Yu,
Eberly Lynn E.
Publication year - 2004
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.1966
Subject(s) - gee , covariate , generalized estimating equation , statistics , estimating equations , observational study , marginal model , econometrics , outcome (game theory) , mathematics , estimation , structural equation modeling , sample size determination , medicine , regression analysis , maximum likelihood , economics , management , mathematical economics
The generalized estimation equation (GEE) method is widely used in longitudinal data analysis, particularly when the outcome variable is non‐Gaussian distributed. Under mild regulatory conditions, the parameter estimates are consistent and their asymptotic variances are efficient. In an observational study focusing on alcoholism patients, we applied the GEE method to longitudinal count data from medical utilization records from a large national managed care organization. The health services research question was whether there was a change in medical utilization for patients after engaging in alcoholism treatment as compared to before treatment. Thus, the main effect of interest was a time‐varying covariate indicating whether the patient had undergone treatment yet or not. GEE under five different working correlations was employed and mixed results regarding the significance of the treatment effect were found. Because of the large sample size, i.e. 8485 patients with an average of 46 repeated measurements per patient, differences across the estimates produced by the different working correlation structures was suspicious. It is shown that these differences are maybe caused by the fact that the time‐varying covariate in the marginal mean model is misspecified. A simulation study is performed to demonstrate that misspecification of the time‐varying covariate in the marginal mean structure can cause differences in GEE results across various choices of working correlation structure. Copyright © 2004 John Wiley & Sons, Ltd.