Title: A flexible parametric modelling framework for survival analysis
Authors: Kevin Burke - University of Limerick (Ireland) [presenting]
Abstract: A general parametric survival modelling framework is introduced which encompasses key shapes of hazard function (constant, increasing, decreasing, up-then-down, down-then-up), various common survival distributions (log-logistic, Burr type XII, Weibull, Gompertz), and includes defective distributions (cure models). This generality is achieved using four distributional parameters: two scale parameters - which, respectively, relate to accelerated failure time (AFT) and proportional hazards (PH) models - and two shape parameters. Furthermore, we advocate ``multi-parameter regression'' (also known as ``distributional regression''), whereby more than one distributional parameter depends on covariates. In particular, we suggest introducing covariates through just one or other of the two scale parameters (covering AFT and PH models), and through a ``power'' shape parameter (covering more complex non-AFT/non-PH effects); the other shape parameter remains covariate-independent, and handles automatic selection of the baseline distribution. We explore inferential performance by way of simulation studies, and demonstrate the effectiveness of the framework using real data analysis.