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A protective estimator for longitudinal binary data subject to non‐ignorable non‐monotone missingness

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

Abstract

Summary. In longitudinal studies missing data are the rule not the exception. We consider the analysis of longitudinal binary data with non‐monotone missingness that is thought to be non‐ignorable. In this setting a full likelihood approach is complicated algebraically and can be computationally prohibitive when there are many measurement occasions. We propose a ‘protective’ estimator that assumes that the probability that a response is missing at any occasion depends, in a completely unspecified way, on the value of that variable alone. Relying on this ‘protectiveness’ assumption, we describe a pseudolikelihood estimator of the regression parameters under non‐ignorable missingness, without having to model the missing data mechanism directly. The method proposed is applied to CD4 cell count data from two longitudinal clinical trials of patients infected with the human immunodeficiency virus.

Suggested Citation

  • Garrett M. Fitzmaurice & Stuart R. Lipsitz & Geert Molenberghs & Joseph G. Ibrahim, 2005. "A protective estimator for longitudinal binary data subject to non‐ignorable non‐monotone missingness," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(4), pages 723-735, November.
    • Handle: RePEc:bla:jorssa:v:168:y:2005:i:4:p:723-735
    DOI: 10.1111/j.1467-985X.2005.00374.x
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    References listed on IDEAS

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    1. White, Halbert, 1982. "Editor's introduction," Journal of Econometrics, Elsevier, vol. 20(1), pages 1-2, October.
    2. Thomas R. Ten Have & Michael E. Miller & Beth A. Reboussin & Margaret K. James, 2000. "Mixed Effects Logistic Regression Models for Longitudinal Ordinal Functional Response Data with Multiple-Cause Drop-Out from the Longitudinal Study of Aging," Biometrics, The International Biometric Society, vol. 56(1), pages 279-287, March.
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    Cited by:

    1. Li, Haocheng & Shu, Di & He, Wenqing & Yi, Grace Y., 2019. "Variable selection via the composite likelihood method for multilevel longitudinal data with missing responses and covariates," Computational Statistics & Data Analysis, Elsevier, vol. 135(C), pages 25-34.
    2. Roula Tsonaka & Dimitris Rizopoulos & Geert Verbeke & Emmanuel Lesaffre, 2010. "Nonignorable Models for Intermittently Missing Categorical Longitudinal Responses," Biometrics, The International Biometric Society, vol. 66(3), pages 834-844, September.

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