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Bayesian inference on longitudinal-survival data with multiple features

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  • Tao Lu

    (University of Nevada)

Abstract

The modeling of longitudinal and survival data is an active research area. Most of researches focus on improving the estimating efficiency but ignore many data features frequently encountered in practice. In this article, we develop a joint model that concurrently accounting for longitudinal-survival data with multiple features. Specifically, our joint model handles skewness, limit of detection, missingness and measurement errors in covariates which are typical observed in the collection of longitudinal-survival data from many studies. We employ a Bayesian approach for making inference on the joint model. The proposed model and method are applied to an AIDS study. A few alternative models under different conditions are compared. Some interesting results are reported. Simulation studies are conducted to assess the performance of the proposed methods.

Suggested Citation

  • Tao Lu, 2017. "Bayesian inference on longitudinal-survival data with multiple features," Computational Statistics, Springer, vol. 32(3), pages 845-866, September.
    • Handle: RePEc:spr:compst:v:32:y:2017:i:3:d:10.1007_s00180-016-0681-3
    DOI: 10.1007/s00180-016-0681-3
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