Bias in estimating association parameters for longitudinal binary responses with drop-outs.
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.
Fitzmaurice
G M
GM
Department of Biostatistics, Harvard School of Public Health, Boston, Massachusetts 02115, USA.
Lipsitz
S R
SR
Molenberghs
G
G
Ibrahim
J G
JG
D000465:Algorithms; D015982:Bias; D001699:Biometry; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical