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A robust joint modeling approach for longitudinal data with informative dropouts

Author

Listed:
  • Weiping Zhang

    (University of Science and Technology of China)

  • Feiyue Xie

    (University of Science and Technology of China)

  • Jiaxin Tan

    (University of Science and Technology of China)

Abstract

This article proposes a robust method for analysing longitudinal continuous responses with informative dropouts and potential outliers by using the multivariate t-distribution. We specify a dropout mechanism and a missing covariate distribution and incorporate them into the complete data log-likelihood. Unlike the existing approaches which mainly focus on the inference of regression mean and dropouts process, our approach aims to reveal the dynamics in the location function, marginal scale function and association by joint parsimonious modeling the location and dependence structure. A parametric fractional imputation algorithm is developed to speed up the computation associated with the EM algorithm for maximum likelihood estimation with missing data. The resulting estimators are shown to be consistent and asymptotically normally distributed. Data examples and simulations demonstrate the effectiveness of the proposed approach.

Suggested Citation

  • Weiping Zhang & Feiyue Xie & Jiaxin Tan, 2020. "A robust joint modeling approach for longitudinal data with informative dropouts," Computational Statistics, Springer, vol. 35(4), pages 1759-1783, December.
    • Handle: RePEc:spr:compst:v:35:y:2020:i:4:d:10.1007_s00180-020-00972-6
    DOI: 10.1007/s00180-020-00972-6
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    References listed on IDEAS

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    Cited by:

    1. Zhan Liu & Chun Yip Yau, 2022. "A propensity score adjustment method for longitudinal time series models under nonignorable nonresponse," Statistical Papers, Springer, vol. 63(1), pages 317-342, February.

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