CANSSI National Seminar Series - Journal Club (DSAS)

Speaker: Christopher Jackson

Senior Investigator Statistician at the MRC Biostatistics Unit at the University of Cambridge, develops statistical methods for health care policy and is the author of several widely applied R packages including msm and flexsurv.

A Comparison of Two Frameworks for Multi-State Modelling, Applied to Outcomes after Hospital Admissions with COVID-19

In this talk, Chris will compare two multi-state modelling frameworks that can be used to represent observed dates of events for a set of individuals.    The methods are applied to data from people admitted to hospital with COVID-19 in England, to estimate the probability of admission to ICU, the probability of death in hospital for patients before and after ICU admission, the lengths of stay in hospital, and how these vary with age and gender. One modelling framework is based on defining transition-specific hazard functions for competing risks. A less commonly-used framework defines partially-latent subpopulations who will experience each subsequent event, and uses a mixture model to estimate the probability that an individual will experience each event, and the distribution of the time to the event given that it occurs. Chris will compare the advantages and disadvantages of these two frameworks, in the context of the COVID-19 example. The issues include the interpretation of the model parameters, the computational efficiency of estimating the quantities of interest, implementation in software and assessing goodness of fit. In the example, it is found that some groups appear to be at very low risk of some events, in particular ICU admission, and these are best represented by using “cure-rate” models to define transition-specific hazards. The models described can be implemented in the R package “flexsurv”, which allows arbitrarily-flexible distributions to be used to represent the cause-specific hazards or times to events.