Joint analysis of longitudinal data and recurrent episodes data with application to medical cost analysis

Liang Zhu, Hui Zhao*, Jianguo Sun, Stanley Pounds, Hui Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

This paper discusses regression analysis of longitudinal data in which the observation process may be related to the longitudinal process of interest. Such data have recently attracted a great deal of attention and some methods have been developed. However, most of those methods treat the observation process as a recurrent event process, which assumes that one observation can immediately follow another. Sometimes, this is not the case, as there may be some delay or observation duration. Such a process is often referred to as a recurrent episode process. One example is the medical cost related to hospitalization, where each hospitalization serves as a single observation. For the problem, we present a joint analysis approach for regression analysis of both longitudinal and observation processes and a simulation study is conducted that assesses the finite sample performance of the approach. The asymptotic properties of the proposed estimates are also given and the method is applied to the medical cost data that motivated this study.

Original languageEnglish (US)
Pages (from-to)5-16
Number of pages12
JournalBiometrical Journal
Volume55
Issue number1
DOIs
StatePublished - Jan 1 2013

Keywords

  • Joint model
  • Longitudinal data analysis
  • Random effect
  • Recurrent episode process

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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