  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.
