The 6th talk within Seminar series in Statistics and Data Science of this semester, will take place at Erling Svedrups plass (Niels Henrik Abel Hus, 8th floor) on TUESDAY, 16.11.2021, 14:15. The talk will be given by Celine Marie Løken Cunen, a Postdoctoral fellow at the Department of Mathematics, University of Oslo.

Access to the seminar area physically at this point is not planned to be restricted. Yet, it will be possible to follow the talk on Zoom, using this link:
https://uio.zoom.us/j/63893623381?pwd=NkVqVFZYeDhqVWMzRzdub1VaRHhTZz09

Title:
Non-Markovian parametric multi-state models for interval censored data

Abstract:
For many real-life phenomena one may assume that the units of observation, typically patients, transition through a set of discrete states on their way towards an absorbing state. The states often constitute various stages of a disease, from perfect health through various stages of dementia for example. Multi-state models are a class of statistical models which allow us to study the time spent in different states, the probability of transitioning between states, and the relationship between these quantities and covariates of interest. In many applications the transition times between states are not observed exactly; instead, the current state of the patients is queried at arbitrary times. The transition times are therefore interval censored, and this makes inference and modelling challenging. Most current approaches are based on the Markov assumption, for example the simplest parametric model available - the time-homogeneous Markov model. Here, we propose a new, general framework for parametric inference with interval censored multi-state data. Our models allow non-Markovian behaviour. I will present the framework and an algorithm for the automatic construction of the likelihood function, along with real-data examples. This talk is based on joint work with Marthe Aastveit and Nils Lid Hjort.

Best regards,
Sven Ove Samuelsen & Aliaksandr Hubin