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Marginal methods for correlated binary data with misclassified responses

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  • Zhijian Chen
  • Grace Y. Yi
  • Changbao Wu

Abstract

Misclassification is a longstanding concern in medical research. Although there has been much research concerning error-prone covariates, relatively little work has been directed to problems with response variables subject to error. In this paper we focus on misclassification in clustered or longitudinal outcomes. We propose marginal analysis methods to handle binary responses which are subject to misclassification. The proposed methods have several appealing features, including simultaneous inference for both marginal mean and association parameters, and they can handle misclassified responses for a number of practical scenarios, such as the case with a validation subsample or replicates. Furthermore, the proposed methods are robust to model misspecification in a sense that no full distributional assumptions are required. Numerical studies demonstrate satisfactory performance of the proposed methods under a variety of settings. Copyright 2011, Oxford University Press.

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  • Zhijian Chen & Grace Y. Yi & Changbao Wu, 2011. "Marginal methods for correlated binary data with misclassified responses," Biometrika, Biometrika Trust, vol. 98(3), pages 647-662.
    • Handle: RePEc:oup:biomet:v:98:y:2011:i:3:p:647-662
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    File URL: http://hdl.handle.net/10.1093/biomet/asr035
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    Cited by:

    1. Qihuang Zhang & Grace Y. Yi, 2023. "Generalized network structured models with mixed responses subject to measurement error and misclassification," Biometrics, The International Biometric Society, vol. 79(2), pages 1073-1088, June.
    2. Brajendra C. Sutradhar, 2022. "Fixed versus Mixed Effects Based Marginal Models for Clustered Correlated Binary Data: an Overview on Advances and Challenges," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(1), pages 259-302, May.
    3. Debjit Sengupta & Tathagata Banerjee & Surupa Roy, 2020. "Estimation of Poisson mean with under‐reported counts: a double sampling approach," Australian & New Zealand Journal of Statistics, Australian Statistical Publishing Association Inc., vol. 62(4), pages 508-535, December.

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