Training course at Lifetime Data Science conference 2023

I’m slightly (very) late in advertising this, but I will be at the 3rd Conference on Lifetime Data Science, to be held in Raleigh, North Carolina, USA, during May 31 – June 2, 2023, teaching a one-day course on Flexible parametric competing risks and multi-state models. Full conference details are here. The programme looks rather spectacular, so I hope to see you there! Keep reading for the course description, and details on further opportunities to take this course.

Course description

This course will focus on the use of parametric survival models when analyzing data with competing risks, and the general setting of multi-state models. Competing risks models play an increasingly important role in 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 when performing a competing risks analysis (e.g., which quantities can be estimated and what they do represent) as well as practical aspects of estimation. Multi-state models provide an extension of competing risks models that also enable the modeling of complex disease profiles. By modeling transitions between disease states and accounting for competing events at each transition, we can gain an improved understanding of a patient’s prognosis and how risk factors impact 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. The course will be taught using example Stata code making use of the multistate and merlin packages written by the lecturer, with R code provided as supplementary material.

Where else will this course be running?

Glad you asked…

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