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Marginal analyses of longitudinal data with an informative pattern of observations

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  • D. M. Farewell

Abstract

We consider solutions to generalized estimating equations with singular working correlation matrices, of which the estimator of Diggle et al. (2007) is a special case. We give explicit conditions for consistent estimation when the pattern of observations may be informative. In such cases, simulations reveal reduced bias and reduced mean squared error compared with existing alternatives. A study of peritoneal dialysis is used to illustrate the methodology. Copyright 2010, Oxford University Press.

Suggested Citation

  • D. M. Farewell, 2010. "Marginal analyses of longitudinal data with an informative pattern of observations," Biometrika, Biometrika Trust, vol. 97(1), pages 65-78.
    • Handle: RePEc:oup:biomet:v:97:y:2010:i:1:p:65-78
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    File URL: http://hdl.handle.net/10.1093/biomet/asp068
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    Cited by:

    1. Aidan G. O’Keeffe & Daniel M. Farewell & Brian D. M. Tom & Vernon T. Farewell, 2016. "Multiple Imputation of Missing Composite Outcomes in Longitudinal Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 8(2), pages 310-332, October.
    2. Shaun R. Seaman & Daniel Farewell & Ian R. White, 2016. "Linear Increments with Non-monotone Missing Data and Measurement Error," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(4), pages 996-1018, December.
    3. Jaeil Ahn & Suyu Liu & Wenyi Wang & Ying Yuan, 2013. "Bayesian Latent-Class Mixed-Effect Hybrid Models for Dyadic Longitudinal Data with Non-Ignorable Dropouts," Biometrics, The International Biometric Society, vol. 69(4), pages 914-924, December.

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