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Identification problem of transition models for repeated measurement data with nonignorable missing values

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  • Morikawa, Kosuke
  • Kano, Yutaka

Abstract

In this paper, we consider a transition model on a response variable to describe repeated measurement data and we provide sufficient conditions to check model identifiability when analyzing data with nonignorable missing values. The sufficient conditions can give us intuitive model characteristics to achieve identifiability. In addition to the model assumptions on the response variable, a parametric model of the missing-data mechanism is often assumed. In this article, we consider identifiability in two situations: (i) both the response variable distribution and the missing-data mechanism are parametric; (ii) one of them is nonparametric, i.e., the global model is semiparametric. Useful identifiable models are proposed on the basis of these conditions. We also present an application to data of a comparative trial of two dosages of depot medroxyprogesterone acetate.

Suggested Citation

  • Morikawa, Kosuke & Kano, Yutaka, 2018. "Identification problem of transition models for repeated measurement data with nonignorable missing values," Journal of Multivariate Analysis, Elsevier, vol. 165(C), pages 216-230.
    • Handle: RePEc:eee:jmvana:v:165:y:2018:i:c:p:216-230
    DOI: 10.1016/j.jmva.2017.12.007
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    References listed on IDEAS

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    1. Sheng Wang & Jun Shao & Jae Kwang Kim, "undated". "An Instrumental Variable Approach for Identification and Estimation with Nonignorable Nonresponse," Mathematica Policy Research Reports a9593fac2c9746f486d2162f9, Mathematica Policy Research.
    2. repec:mpr:mprres:8160 is not listed on IDEAS
    3. Gong Tang, 2003. "Analysis of multivariate missing data with nonignorable nonresponse," Biometrika, Biometrika Trust, vol. 90(4), pages 747-764, December.
    4. Jolene Birmingham & Andrea Rotnitzky & Garrett M. Fitzmaurice, 2003. "Pattern–mixture and selection models for analysing longitudinal data with monotone missing patterns," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 275-297, February.
    5. Jiwei Zhao & Jun Shao, 2015. "Semiparametric Pseudo-Likelihoods in Generalized Linear Models With Nonignorable Missing Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(512), pages 1577-1590, December.
    6. Kenneth J. Wilkins & Garrett M. Fitzmaurice, 2006. "A Hybrid Model for Nonignorable Dropout in Longitudinal Binary Responses," Biometrics, The International Biometric Society, vol. 62(1), pages 168-176, March.
    7. Ying Yuan & Roderick J. A. Little, 2009. "Mixed-Effect Hybrid Models for Longitudinal Data with Nonignorable Dropout," Biometrics, The International Biometric Society, vol. 65(2), pages 478-486, June.
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    Cited by:

    1. Majid Mojirsheibani, 2022. "On the maximal deviation of kernel regression estimators with NMAR response variables," Statistical Papers, Springer, vol. 63(5), pages 1677-1705, October.
    2. Mojirsheibani, Majid, 2021. "On classification with nonignorable missing data," Journal of Multivariate Analysis, Elsevier, vol. 184(C).

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