Penalised Piecewise Exponential Distributional Regression Model for Survival Analysis

Jack Moore, University of Limerick

Co-authors: Shirin Moghaddam, University of Limerick; Kevin Burke, University of Limerick

Abstract: The field of survival analysis is concerned with modelling of time-to-event data. One of the key applications areas of survival analysis is medical research, where interest lies in survival times of patients, e.g., time to onset or recurrence of a disease. Traditional parametric modelling approaches rely on distributions. These impose strong assumptions on the data at hand. In contrast, the piecewise exponential model offers a more general parametric modelling approach: it has the capability of approximating any survival distribution, without prior knowledge of the underlying distribution of the data. Despite this versatility, it has historically been under-utilized. This can perhaps be explained by the popularity of the Cox model. However, in recent years, there has been a resurgence of interest in the piecewise exponential model, with various developments aimed at enhancing its performance and utility.

In this poster, we introduce the piecewise exponential model and present extensions aimed at improving the viability of this model. Specifically, we make use of a distributional regression structure. Thus, our framework enables flexible modelling of both the underlying baseline hazard and the nature of covariate effects, where the intervals/ pieces and covariates of our model are selected automatically via an adaptive lasso penalisation.