Competing Risks and Multi-state Models
Course 6
Date
18–20 January 2024
Faculty
Dr. Michael Crowther
Red Door Analytics, Stockholm, Sweden
Dr. Caroline Dietrich
Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden and Red Door Analytics, Stockholm, Sweden
Venue
CH – 3823 Wengen | Hotel Edelweiss
Course description
This course will focus on how to analyse and interpret data in the competing risk, and the more general, multi-state model setting. Competing risks models play an increasingly important role for predicting absolute risks of disease and prognosis using time to event data. An overarching goal of this course is to provide a solid introduction to important concepts in the presence of competing risks (e.g., which quantities can be estimated and what they represent) as well as practical aspects of estimation. Multi-state models provide an extension to the competing risks situation, enabling modelling of complex disease pathways. By modelling transitions between disease states, accounting for competing events at each transition, we can gain an improved understanding of a patient’s prognosis and how risk factors impact over the whole disease pathway. Throughout the course 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 focus on obtaining clinically useful and directly interpretable predictions, which are particularly useful for more complex models, but also describe the challenges and various approaches to calculating them. We will also discuss assumptions of the models, including the Markov assumption and how this can be relaxed. Real-world examples will be presented and discussed. The course will be taught using Stata making use of the multistate and merlin packages.
Course objectives
By the end of this course participants will have:
- An understanding of how to fit and interpret flexible parametric survival models, including modeling of time-dependent effects.
- An understanding of competing risks models and how to estimate cumulative incidence functions non-parametrically and using parametric models.
- An understanding of how to construct, analyse and interpret a multi-state model.
- Practical experience of fitting the models using Stata®.
Course audience
Course participants should be familiar with standard survival models, such as the Cox model and/or parametric survival models (e.g. Weibull) and interested in extending their knowledge to the more complex issues of competing risks and multistate models. The course will include theory but emphasis is placed on applying and interpreting the methods.
Course outline
Thursday, 18 January 8:00 am – 12:00 pm | 4:30 pm – 6:30 pm
Friday, 19 January 8:00 am – 12:00 pm | 4:30 pm – 6:30 pm
Saturday, 20 January 8:00 am – 12:00 pm | 1:00 pm – 3:00 pm
Day 1
- Brief review of time-to-event data including the Cox model
- Flexible parametric survival models
- Modelling competing risks
- Estimating cumulative incidence functions
Day 2
- Introduction to multi-state models
- The illness death model
- The Markov assumption
- Stacked versus separate models
- Estimating contrasts between groups
Day 3
- Expected length of stay in different states
- Resetting the clock and semi-Markov models
- Standardisation in multi-state models
- Real-world examples using multi-state models
Credits
1.0 ECTS
Course materials
Bring a portable computer. A course license for Stata® will be available to install before arrival. Participants will receive digital copies of all course material.
Onsite University of Bern IT staff provides support upon e-mail () request.
Course fee
PhD Bern: CHF 600
PhD other: CHF 800
Academic: CHF 1000
Industry: CHF 2000
Registration
Accomodation
Book your accommodation separately. Please see recommendations for special prices.