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A Flexible Parametric Modelling Framework for Survival Analysis by Burke, Kevin; Jones, M. C.; Noufaily, Angela is a Mathematics article available to read on EtoBox.

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## Summary We introduce a general, flexible, parametric survival modelling framework which encompasses key shapes of hazard functions (constant; increasing; decreasing; up then down; down then up) and various common survival distributions (log-logistic; Burr type XII; Weibull; Gompertz) and includes defective distributions (cure models). This generality is achieved by using four distributional parameters: two scale-type parameters—one of which relates to accelerated failure time (AFT) modelling; the other to proportional hazards (PH) modelling—and two shape parameters. Furthermore, we advocate ‘multiparameter regression’ whereby more than one distributional parameter depends on covariates—rather than the usual convention of having a single covariate-dependent (scale) parameter. This general formulation unifies the most popular survival models, enabling us to consider the practical value of possible modelling choices. 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 or non-PH effects); the other shape parameter remains covariate ind

Who reads A Flexible Parametric Modelling Framework for Survival Analysis?

Author

Burke, Kevin; Jones, M. C.; Noufaily, Angela

Publisher

Oxford University Press (OUP)

Published

2020

Language

EN

Field

Mathematics (Physical Sciences)