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

TL;DR

The paper introduces MEGH, a flexible parametric mixed-effects hazard model for clustered survival data, accommodating between-cluster variability and generalizing existing models, with implementation and diagnostic tools.

Contribution

It develops a novel general hazard model for clustered survival data, unifying and extending existing mixed-effects models, with estimation algorithms and practical diagnostics.

Findings

Effective in modeling clustered survival data.

Performs well in simulations and real leukemia data.

Provides comprehensive diagnostic tools.

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 (MEPH) and mixed-effects accelerated failure time (MEAFT) 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 {\tt 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 leukemia.