gsem: A Stata command for parametric joint modelling of longitudinal and accelerated failure time models

Dil E., KARASOY D.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, vol.196, 2020 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 196
  • Publication Date: 2020
  • Doi Number: 10.1016/j.cmpb.2020.105612
  • Journal Name: COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, BIOSIS, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, EMBASE, INSPEC, MEDLINE
  • Keywords: gsem, Parametric joint modelling, Stata, Longitudinal data, Survival data, PROPORTIONAL HAZARDS, LIKELIHOOD APPROACH, SURVIVAL
  • TED University Affiliated: Yes

Abstract

Background: The number of studies using joint modelling of longitudinal and survival data have increased in the past two decades, but analytical techniques and software shortcomings have remained. A joint model is often used for analysis of a combination of longitudinal sub-model and survival sub-model using shared random effects. Cox regression commonly referring to the survival sub-model, should not be used when proportional hazards assumptions are not satisfied. In such cases, the parametric survival model is preferable.