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Inference for longitudinal data with nonignorable nonmonotone missing responses

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  • Sinha, Sanjoy K.
  • Kaushal, Amit
  • Xiao, Wenzhong

Abstract

For the analysis of longitudinal data with nonignorable and nonmonotone missing responses, a full likelihood method often requires intensive computation, especially when there are many follow-up times. The authors propose and explore a Monte Carlo method, based on importance sampling, for approximating the maximum likelihood estimators. The finite-sample properties of the proposed estimators are studied using simulations. An application of the proposed method is also provided using longitudinal data on peptide intensities obtained from a proteomics experiment of trauma patients.

Suggested Citation

  • Sinha, Sanjoy K. & Kaushal, Amit & Xiao, Wenzhong, 2014. "Inference for longitudinal data with nonignorable nonmonotone missing responses," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 77-91.
    • Handle: RePEc:eee:csdana:v:72:y:2014:i:c:p:77-91
    DOI: 10.1016/j.csda.2013.10.027
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    References listed on IDEAS

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