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

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

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.

Suggested Citation

  • Garrett M. Fitzmaurice & Stuart R. Lipsitz & Geert Molenberghs & Joseph G. Ibrahim, 2001. "Bias in Estimating Association Parameters for Longitudinal Binary Responses with Drop‐Outs," Biometrics, The International Biometric Society, vol. 57(1), pages 15-21, March.
    • Handle: RePEc:bla:biomet:v:57:y:2001:i:1:p:15-21
    DOI: 10.1111/j.0006-341X.2001.00015.x
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    References listed on IDEAS

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    1. Stuart R. Lipsitz & Nan M. Laird & David P. Harrington, 1992. "A Three‐Stage Estimator for Studies with Repeated and Possibly Missing Binary Outcomes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(1), pages 203-213, March.
    2. D. R. Cox, 1972. "The Analysis of Multivariate Binary Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 21(2), pages 113-120, June.
    3. Stuart R. Lipsitz & Geert Molenberghs & Garrett M. Fitzmaurice & Joseph Ibrahim, 2000. "GEE with Gaussian Estimation of the Correlations When Data Are Incomplete," Biometrics, The International Biometric Society, vol. 56(2), pages 528-536, June.
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    Cited by:

    1. Fang, Fang & Shao, Jun, 2016. "Iterated imputation estimation for generalized linear models with missing response and covariate values," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 111-123.
    2. Joseph Ibrahim & Geert Molenberghs, 2009. "Missing data methods in longitudinal studies: a review," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 18(1), pages 1-43, May.
    3. Stuart R. Lipsitz & Garrett M. Fitzmaurice & Joseph G. Ibrahim & Debajyoti Sinha & Michael Parzen & Steven Lipshultz, 2009. "Joint generalized estimating equations for multivariate longitudinal binary outcomes with missing data: an application to acquired immune deficiency syndrome data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 3-20, January.
    4. Andrew Copas & Shaun Seaman, 2010. "Bias from the use of generalized estimating equations to analyze incomplete longitudinal binary data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(6), pages 911-922.
    5. Baojiang Chen & Xiao-Hua Zhou, 2011. "Doubly Robust Estimates for Binary Longitudinal Data Analysis with Missing Response and Missing Covariates," Biometrics, The International Biometric Society, vol. 67(3), pages 830-842, September.

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