Joint modeling of longitudinal, recurrent events and failure time data for survivor's population

Qing Cai, Mei Cheng Wang, Kwun Chuen Gary Chan

Research output: Contribution to journalArticlepeer-review

Abstract

Recurrent events together with longitudinal measurements are commonly observed in follow-up studies where the observation is terminated by censoring or a primary failure event. In this article, we developed a joint model where the dependence of longitudinal measurements, recurrent event process and time to failure event is modeled through rescaling the time index. The general idea is that the trajectories of all biology processes of subjects in the survivors’ population are elongated or shortened by the rate identified from a model for the failure event. To avoid making disputing assumptions on recurrent events or biomarkers after the failure event (such as death), the model is constructed on the basis of survivors’ population. The model also possesses a specific feature that, by aligning failure events as time origins, the backward-in-time model of recurrent events and longitudinal measurements shares the same parameter values with the forward time model. The statistical properties, simulation studies and real data examples are conducted. The proposed method can be generalized to analyze left-truncated data.

Original languageEnglish (US)
Pages (from-to)1150-1160
Number of pages11
JournalBiometrics
Volume73
Issue number4
DOIs
StatePublished - 2017

Keywords

  • Backward process model
  • Counting process
  • Informative censoring
  • Informative sampling
  • Left truncation
  • Time-adjusted model

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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