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Mean estimating equation approach to analysing cluster-correlated data with nonignorable cluster sizes

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  • E. Benhin
  • J. N. K. Rao
  • A. J. Scott

Abstract

Most methods for analysing cluster-correlated biological data implicitly assume the ignorability of cluster sizes. When this assumption fails, the resulting inferences may be asymptotically invalid. Hoffman et al. (2001) proposed a simple but computationally intensive method, based on a large number of within-cluster resamples and associated separate estimating equations, that leads to asymptotically valid inferences whether the cluster sizes are ignorable or not. We study a simple method, based on a single inverse cluster size-weighted estimating equation, that avoids resampling and yet leads to asymptotically valid inferences. Simulation results are presented to assess the performance of the proposed method. We also propose Wald tests for ignorability of cluster sizes. Copyright 2005, Oxford University Press.

Suggested Citation

  • E. Benhin & J. N. K. Rao & A. J. Scott, 2005. "Mean estimating equation approach to analysing cluster-correlated data with nonignorable cluster sizes," Biometrika, Biometrika Trust, vol. 92(2), pages 435-450, June.
    • Handle: RePEc:oup:biomet:v:92:y:2005:i:2:p:435-450
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    File URL: http://hdl.handle.net/10.1093/biomet/92.2.435
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    Cited by:

    1. Glen McGee & Marianthi‐Anna Kioumourtzoglou & Marc G. Weisskopf & Sebastien Haneuse & Brent A. Coull, 2020. "On the interplay between exposure misclassification and informative cluster size," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1209-1226, November.
    2. Xiuyu J. Cong & Guosheng Yin & Yu Shen, 2007. "Marginal Analysis of Correlated Failure Time Data with Informative Cluster Sizes," Biometrics, The International Biometric Society, vol. 63(3), pages 663-672, September.
    3. Jaakko Nevalainen & Somnath Datta & Hannu Oja, 2014. "Inference on the marginal distribution of clustered data with informative cluster size," Statistical Papers, Springer, vol. 55(1), pages 71-92, February.
    4. Federico Bugni & Ivan Canay & Azeem Shaikh & Max Tabord-Meehan, 2022. "Inference for Cluster Randomized Experiments with Non-ignorable Cluster Sizes," Papers 2204.08356, arXiv.org, revised Apr 2024.
    5. Shaun R. Seaman & Menelaos Pavlou & Andrew J. Copas, 2014. "Methods for observed-cluster inference when cluster size is informative: A review and clarifications," Biometrics, The International Biometric Society, vol. 70(2), pages 449-456, June.
    6. Jae Kwang Kim & J.N.K. Rao & Yonghyun Kwon, 2022. "Analysis of clustered survey data based on two‐stage informative sampling and associated two‐level models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1522-1540, October.
    7. An Creemers & Marc Aerts & Niel Hens & Ziv Shkedy & Frank De Smet & Philippe Beutels, 2011. "Revealing age-specific past and future unrelated costs of pneumococcal infections by flexible generalized estimating equations," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(8), pages 1533-1547, August.

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