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Title: Hierarchical multi-parameter regression survival models Authors:  Fatima-Zahra Jaouimaa - University of Limerick (Ireland) [presenting]
Il Do Ha - Pukyong National University (Korea, South)
Kevin Burke - University of Limerick (Ireland)
Abstract: Standard survival models introduce covariates through a single (scale) parameter, and we refer to this standard practice as Single-Parameter Regression (SPR). In contrast, Multi-Parameter Regression (MPR) allows covariates to enter the model through multiple distributional parameters, i.e., scale and shape. This approach to modelling has been shown to produce flexible and robust models with a relatively low model complexity cost. However, it is very common to have clustered data arising from survival analysis studies, and this is something that is underdeveloped in the MPR context. Therefore, we extend MPR models to handle multivariate survival data by introducing random effects in both the scale and the shape regression components. We consider a variety of possible dependence structures for these random effects (independent, shared, and correlated), and estimation proceeds using a h-likelihood approach. As the shape parameter may be viewed as a dispersion parameter for log-time, our proposal bears similarities to Double Hierarchical Generalized Linear Modelling (DHGLM). We investigate the performance of our estimation procedure using simulated data, and also consider a real data example.