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In many applications of survival data analysis, the individuals are treatedin different medical centres or belong to different clusters defined bygeographical or administrative regions. The analysis of such data requiresaccounting for between-cluster variability. Ignoring such variability wouldimpose unrealistic assumptions in the analysis and could affect the inferenceon the statistical models. We develop a novel parametric mixed-effects generalhazard (MEGH) model that is particularly suitable for the analysis of clusteredsurvival data. The proposed structure generalises the mixed-effectsproportional hazards (MEPH) and mixed-effects accelerated failure time (MEAFT)structures, among other structures, which are obtained as special cases of theMEGH structure. We develop a likelihood-based algorithm for parameterestimation in general subclasses of the MEGH model, which is implemented in ourR package {\tt MEGH}. We propose diagnostic tools for assessing the randomeffects and their distributional assumption in the proposed MEGH model. Weinvestigate the performance of the MEGH model using theoretical and simulationstudies, as well as a real data application on leukemia.

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