MEGH: A parametric class of general hazard models for clustered survival data

Rubio, Javier; Drikvandi, Reza

Abstract

In many applications of survival data analysis, the individuals are treated in different medical centres or belong to different clusters defined by geographical or administrative regions. The analysis of such data requires accounting for between-cluster variability. Ignoring such variability would impose unrealistic assumptions in the analysis and could affect the inference on the statistical models. We develop a novel parametric mixed-effects general hazard (MEGH) model that is particularly suitable for the analysis of clustered survival data. The proposed structure generalises the mixed-effects proportional hazards and mixed-effects accelerated failure time structures, among other structures, which are obtained as special cases of the MEGH structure. We develop a likelihood-based algorithm for parameter estimation in general subclasses of the MEGH model, which is implemented in our R package MEGH. We propose diagnostic tools for assessing the random effects and their distributional assumption in the proposed MEGH model. We investigate the performance of the MEGH model using theoretical and simulation studies, as well as a real data application on leukaemia.

Citation

Rubio, J., & Drikvandi, R. (2022). MEGH: A parametric class of general hazard models for clustered survival data. Statistical Methods in Medical Research, 31(8), 1603-1616. https://doi.org/10.1177/09622802221102620

• Journal Article Type: Article
• Acceptance Date: May 1, 2022
• Online Publication Date: Jun 6, 2022
• Publication Date: Aug 1, 2022
• Deposit Date: May 12, 2022
• Publicly Available Date: Jan 13, 2023
• Journal: Statistical Methods in Medical Research
• Print ISSN: 0962-2802
• Electronic ISSN: 1477-0334
• Publisher: SAGE Publications
• Peer Reviewed: Peer Reviewed
• Volume: 31
• Issue: 8
• Pages: 1603-1616
• DOI: https://doi.org/10.1177/09622802221102620
• Public URL: https://durham-repository.worktribe.com/output/1206348