25 January 2020
on course website
Parametric Competing Risks and Multi-state models
This course will focus on the use of parametric survival models when analysing data with competing risks and then extending to multi-state models. Multi-state models are increasingly being used to model complex disease profiles. By modelling transitions between disease states, accounting for competing events at each transition, we can gain an improved understanding of patients prognosis and how risk factors impact over the whole disease pathway. We will place emphasis on the use of flexible parametric survival models that incorporate restricted cubic splines on the log hazard or log cumulative hazard scale. This will include models with time-dependent effects (non-proportional hazards). We will use an efficient and generalizable simulation method to obtain clinically useful and directly interpretable predictions, which are particularly useful for more complex models, but also explain when analytical approaches can be used. We will also discuss assumptions of the models, including the Markov assumption and how this can be relaxed. The course will be taught using Stata making use of the multistate package.
Prof. Paul Lambert, University of Leicester and Karolinska Institutet
Dr Michael Crowther, Biostatistics Research Group, Department of Health Sciences, University of Leicester, UK
Course participants should be familiar with standard survival models, such as the Cox model and/or parametric survival models (e.g. Weibull) and be interested in extending their knowledge to the more complex issues of competing risks and multistate models.
The course will discuss the theory but emphasis will be placed on applying and interpreting the methods.
By the end of this short course participants will have
- An understanding of how to fit and interpret flexible parametric survival models, including Royston-Parmar models.
- An understanding of fitting and interpreting time-dependent effects.
- An understanding of competing risks models and how to estimate cumulative incidence functions using parametric models.
- An understanding of how to construct, analyse and interpret a multi-state model.
- An understanding of the variety of useful measures that can be obtained from multistate models.
- Practical experience of fitting the models using Stata®.
CHF 900: SSPH+ students: CHF 700
Academic fee: CHF 900
Industry fee: CHF 2’000
Participants must book their accommodations themselves (see map and recommendations on www.epi-winterschool.org/hotels.
You can register on the Winter School website www.epi-winterschool.org
on course website