Cardiovascular outcome trials often have a composite outcome - e.g. Death, Stroke, MI, and Hospitalisation - lumped all together than analysed as time to event.
More recently, the “win ratio” has started to be advocated.
Alternatively, Prof. Harrell’s Markov Longitudinal Ordinal Model [Markov Longitudinal Ordinal Model] has been proposed - and has showed improved power and explainability within COVID trials and others.
If we were to use this approach to cardiovascular outcome trials - what are the thoughts on adapting the ordinal (for the say 4 events above) so it can be practically used?
Death - remains death and an absorbing state.
Presumably - we don’t want to model MI or Stroke as an event that occurs on a single day - but once a patients experiences it - continues to be in this state (but can still climb to even worse states).
We would add in an additional state of having had a stroke and an MI at some point in the past.
How should we deal with hospitalisation - as an type of event like I propose for stroke and MI above - or as a state you can be in that day. Perhaps if the former - we need additional states of hospitalised once, twice, thrice etc…or perhaps we should switch to using it as a daily state so can calculate number of days in hospital (which would be helpful for cost effectively analysis).
Had MI and Stroke
Been hospitalised 3+ times
Been hospitalised 2 times
Been hospitalised 1 times
Alive + Well
Welcome any thoughts or suggestions - or indeed if any empirical data…