A modified two-stage approach for joint modelling of longitudinal and time-to-event data

Pham Huong, Darfiana Nur, pham hoa, alan branford

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Joint models for longitudinal and time-to-event data have been applied in many different fields of statistics and clinical studies. However, the main difficulty these models have to face with is the computational problem. The requirement for numerical integration becomes severe when the dimension of random effects increases. In this paper, a modified two-stage approach has been proposed to estimate the parameters in joint models. In particular, in the first stage, the linear mixed-effects models and best linear unbiased predictorsare applied to estimate parameters in the longitudinal submodel. In the second stage, an approximation of the fully joint log-likelihood is proposed using the estimated the values of these parameters from the longitudinal submodel. Survival parameters are estimated bymaximizing the approximation of the fully joint log-likelihood. Simulation studies show that the approach performs well, especially when the dimension of random effects increases. Finally, we implement this approach on AIDS data.

Original languageEnglish
Pages (from-to)3379-3398
Number of pages20
JournalJOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
Volume88
Issue number17
DOIs
Publication statusPublished - Nov 2018

Keywords

  • joint models
  • longitudinal data
  • shared random effects approach
  • Survival data
  • two-stage approach

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