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Bayesian quantile regression for analyzing ordinal longitudinal responses in the presence of non-ignorable missingness

Author

Listed:
  • Siamak Ghasemzadeh

    (Shahid Beheshti University)

  • Mojtaba Ganjali

    (Shahid Beheshti University)

  • Taban Baghfalaki

    (Tarbiat Modares University)

Abstract

In this paper, we develop a quantile regression model for analyzing ordinal longitudinal responses with random effects in the presence of non-ignorable and non-monotone missing data. The ordinal responses are related to underlying latent variables which are considered to have asymmetric Laplace distribution. For modeling the missing data mechanism an ordinary probit model is used via specifying another latent variable. In order to consider non-ignorable missing data, a shared parameter model is used for joint modeling of ordinal longitudinal responses and missing data mechanism. We use a Bayesian approach via Markov chain Monte Carlo method for analyzing the proposed joint model. Especially to estimate unknown parameters, a Gibbs sampler algorithm is used. Moreover, we use the Schizophrenia data set to illustrate an application of the proposed model.

Suggested Citation

  • Siamak Ghasemzadeh & Mojtaba Ganjali & Taban Baghfalaki, 2018. "Bayesian quantile regression for analyzing ordinal longitudinal responses in the presence of non-ignorable missingness," METRON, Springer;Sapienza Università di Roma, vol. 76(3), pages 321-348, December.
    • Handle: RePEc:spr:metron:v:76:y:2018:i:3:d:10.1007_s40300-018-0136-4
    DOI: 10.1007/s40300-018-0136-4
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    References listed on IDEAS

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    Cited by:

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    3. Aiai Yu & Yujie Zhong & Xingdong Feng & Ying Wei, 2023. "Quantile regression for nonignorable missing data with its application of analyzing electronic medical records," Biometrics, The International Biometric Society, vol. 79(3), pages 2036-2049, September.

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