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Jointly modeling skew longitudinal survival data with missingness and mismeasured covariates

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  • Tao Lu

Abstract

Jointly modeling longitudinal and survival data has been an active research area. Most researches focus on improving the estimating efficiency but ignore many data features frequently encountered in practice. In the current study, we develop the joint models that concurrently accounting for longitudinal and survival data with multiple features. Specifically, the proposed model handles skewness, missingness and measurement errors in covariates which are typically observed in the collection of longitudinal survival data from many studies. We employ a Bayesian inferential method to make inference on the proposed model. We applied the proposed model to an real data study. A few alternative models under different conditions are compared. We conduct extensive simulations in order to evaluate how the method works.

Suggested Citation

  • Tao Lu, 2017. "Jointly modeling skew longitudinal survival data with missingness and mismeasured covariates," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(13), pages 2354-2367, October.
    • Handle: RePEc:taf:japsta:v:44:y:2017:i:13:p:2354-2367
    DOI: 10.1080/02664763.2016.1254728
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