Bias in Estimating Association Parameters for Longitudinal Binary Responses with Drop‐Outs

Garrett M. Fitzmaurice, Stuart R. Lipsitz, Geert Molenberghs and Joseph G. Ibrahim

Biometrics, 2001, vol. 57, issue 1, 15-21

Abstract: Summary. 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 (GEEZ) 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.

Date: 2001
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