Determining transition probabilities based on PFS and OS curves
I am new to TreeAge and as part of a collaboration am trying to create a cost-effectiveness analysis of 4 first-line treatments for a specific cancer.
It seems PartSA can take the difference between PFS and OS to generate the Markov transition probabilities of Progression-free --> Death, Progression-free --> Progression, and Progression --> Death.
However, there some event-based elements I need to include like severe AEs and I don't believe the continuous accumulation of cost is accurate to my model as patients typically get treatments every 3 weeks rather than daily. So it makes more sense to me to have the model run as 3-week cycles.
Does anyone have a reference for how one can use only the data published in Phase III clinical trials to generate the necessary transition probabilities for the Markov model?
The issue I see with a previous answer is that it cites a NICE technology evaluation and the probability of death during progression free survival was calculated by actually counting the patients using the raw data.
Any guidance would be greatly appreciated.
Comments
There are no event probabilities in a Partitioned Survival Model, so you probably will be better off with a Markov model. Markov models provide more flexibility for individual events with probabilities, costs, etc.
To do this in a Partitioned Survival Model, you would have to average out the cost of events over time, which I do not recommend.
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