Partially linear mixed-effects joint models for skewed and missing longitudinal competing risks outcomes

Tao Lu, Minggen Lu, Min Wang, Jun Zhang, Guang Hui Dong, Yong Xu

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Longitudinal competing risks data frequently arise in clinical studies. Skewness and missingness are commonly observed for these data in practice. However, most joint models do not account for these data features. In this article, we propose partially linear mixed-effects joint models to analyze skew longitudinal competing risks data with missingness. In particular, to account for skewness, we replace the commonly assumed symmetric distributions by asymmetric distribution for model errors. To deal with missingness, we employ an informative missing data model. The joint models that couple the partially linear mixed-effects model for the longitudinal process, the cause-specific proportional hazard model for competing risks process and missing data process are developed. To estimate the parameters in the joint models, we propose a fully Bayesian approach based on the joint likelihood. To illustrate the proposed model and method, we implement them to an AIDS clinical study. Some interesting findings are reported. We also conduct simulation studies to validate the proposed method.

Original languageEnglish
Pages (from-to)971-989
Number of pages19
JournalJournal of Biopharmaceutical Statistics
Issue number6
StatePublished - Nov 2 2019


  • Bayesian inference
  • competing risks
  • longitudinal survival data
  • partially linear mixed-effects models
  • proportional hazard models


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