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

Rubio, Javier; Drikvandi, Reza

doi:10.1177/09622802221102620

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

Rubio, Javier; Drikvandi, Reza

Authors

Javier Rubio

Dr Reza Drikvandi reza.drikvandi@durham.ac.uk
Associate Professor

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

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