Quantifying direct and indirect effect for longitudinal mediator and survival outcome using joint modeling approach

Cheng Zheng, Lei Liu

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Longitudinal biomarkers are widely used in biomedical and translational researches to monitor the progressions of diseases. Methods have been proposed to jointly model longitudinal data and survival data, but its causal mechanism is yet to be investigated rigorously. Understanding how much of the total treatment effect is through the biomarker is important in understanding the treatment mechanism and evaluating the biomarker. In this work, we propose a causal mediation analysis method to compute the direct and indirect effects, when a joint modeling approach is used to take the longitudinal biomarker as the mediator and the survival endpoint as the outcome. Such a joint modeling approach allows us to relax the commonly used “sequential ignorability” assumption. We demonstrate how to evaluate longitudinally measured biomarkers using our method with two case studies, an AIDS study and a liver cirrhosis study.

Original languageEnglish (US)
Pages (from-to)1233-1243
Number of pages11
JournalBiometrics
Volume78
Issue number3
DOIs
StatePublished - Sep 2022

Keywords

  • causal inference
  • joint modeling
  • longitudinal data
  • mediation analysis
  • survival data

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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