Document Server@UHasselt >
Research >
Research publications >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/365

Title: GEE with Gaussian estimation of the correlations when data are incomplete
Authors: Lipsitz, Stuart
Molenberghs, Geert
Fitzmaurice, Garrett
Ibrahim, Joseph
Keywords: Categorical data
Missing data
Longitudinal data
Issue Date: 2000
Citation: Biometrics, 56(2). p. 528-536
Abstract: This paper considers a modification of generalized estimating equations (GEE) for handling missing binary response data. The proposed method uses Gaussian estimation of the correlation parame- ters, i.e., the estimating function that yields an estimate of the correlation parameters is obtained from the multivariate normal likelihood. The proposed method yields consistent estimates of the regression param- eters when data are missing completely at random (MCAR). However, when data are missing at random (MAR), consistency may not hold. In a simulation study with repeated binary outcomes that are missing at random, the magnitude of the potential bias that can arise is examined. The results of the simulation study indicate that, when the working correlation matrix is correctly specified, the bias is almost negligible for the modified GEE. In the simulation study, the proposed modification of GEE is also compared to the standard GEE, multiple imputation, and weighted estimating equations approaches. Finally, the proposed method is illustrated using data from a longitudinal clinical trial comparing two therapeutic treatments, zidovudine (AZT) and didanosine (ddI), in patients with HIV.
URI: http://hdl.handle.net/1942/365
DOI: 10.1111/j.0006-341X.2000.00528.x
ISI #: 000087677500028
ISSN: 0006-341X
Category: A1
Type: Journal Contribution
Validation: ecoom, 2001
Appears in Collections: Research publications

Files in This Item:

Description SizeFormat
Published version896.58 kBAdobe PDF

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.