Journal
BIOMETRICS
Volume 57, Issue 1, Pages 15-21Publisher
INTERNATIONAL BIOMETRIC SOC
DOI: 10.1111/j.0006-341X.2001.00015.x
Keywords
generalized estimating equations; missing data; repeated measures
Funding
- NIEHS NIH HHS [ES07142] Funding Source: Medline
- NIGMS NIH HHS [GM29745] Funding Source: Medline
- NIMH NIH HHS [MH17119] Funding Source: Medline
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This paper considers the impact of bias in the estimation of the association parameters for longitudinal binary responses when there are drop-outs. A number of different estimating equation approaches are considered for the case where drop-out cannot be assumed to be a completely random process. In particular, standard generalized estimating equations (GEE), GEE based on conditional residuals, GEE based on multivariate normal estimating equations for the covariance matrix, and second-order estimating equations (GEE2) are examined. These different GEE estimators are compared in terms of finite sample and asymptotic bias under a variety of drop-out processes. Finally, the relationship between bias in the estimation of the association parameters and bias in the estimation of the mean parameters is explored.
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