Modeling Longitudinal-Competing Risks Data With Skew Distribution and Mismeasured Covariate

Research output: Contribution to journalArticle

Abstract

The study on relationship between HIV viral load and CD4 counts is critical for AIDS treatment. We study the varying relationship between viral load and CD4 counts by accounting for factors usually encountered in practice: skewed distribution in data, competing risks time-to-event, and mismeasured covariate. We propose a joint modeling approach to take into account all these factors. A Bayesian approach is adopted to make inference on the joint model. The proposed model and method are applied to an AIDS study. To testify the validity of the method, simulation studies are performed.

Original languageEnglish
Pages (from-to)73-84
Number of pages12
JournalStatistics in Biopharmaceutical Research
Volume9
Issue number1
DOIs
StatePublished - Jan 2 2017

Keywords

  • Bayesian inference
  • Competing risks
  • Longitudinal data
  • Measurement error
  • Mixed-effects models

Fingerprint Dive into the research topics of 'Modeling Longitudinal-Competing Risks Data With Skew Distribution and Mismeasured Covariate'. Together they form a unique fingerprint.

  • Cite this