Lectures and seminars ARC seminar with Nurgul Batyrbekova: "Modelling multiple time-scale with flexible parametric survival models"

"Modelling multiple time-scale with flexible parametric survival models"

Presenter

Nurgul Batyrbekova, Karolinska Institutet, Department of Medical Epidemiology and Biostatistics

Abstract

There are situations when we need to model data with multiple time-scales. We usually use Cox or Poisson models to model data with multiple time-scales. This would require fine-splitting of the data to ensure that the assumption of proportional hazards within given time-intervals holds. However, this is not optimal when the dataset is large and when the models become too complex to manage. Iacobelli and Carstensen (2013) have shown how to fit multiple time-scales using Poisson models within a multistate framework. The drawback with Poisson method, is that users are required to assume constant hazard rates within time-intervals. An alternative approach is to fit flexible parametric survival models when modelling data with multiple time-scales. This method does not require assumtion of constant hazard rates, splitting data into small time-intervals, and therefore saves time, helps avoid technological limitations and reduces room for error.