A very simple model to serve as an exploration of creating health economic Discrete-Event Simulation (DES) models in Python using SimPy library. The clinical context of the model is as follows:
- During a cycle of treatment, patients can either die (p = 0.15) or experience a full cycle of treatment without any other events occurring.
- Patients can receive up to five cycles of treatment.
- A full cycle causes a longer delay or timeout compared to dying during a cycle. These timeouts are drawn from a Gamma distribution.
- After surviving the maximum number of treatment cycles, patients will enter a follow up phase, in which patients can also die.
The health economic context is as follows:
- Each cycle of treatment incurs 5000 euro initially and 250 euro per day.
- Followup costs 3500 euro per patient as a lumpsum.
- Health-related quality of life (QoL) in the treatment phase is 0.7 as expressed over the duration of one year.
- QoL in the followup phase is 0.8 as expressed over the duration of one year.
Furthermore, all patients are simulated at t = 0, so the interarrival time = 0. Note that the model currently only represents one comparator. So for an actual cost-effectiveness analysis, another comparator must be added, but this is very straightforward.
Three classes are created for the model: 1) a class g contains all constants, 2) a Patient class in which attributes of patients are set and 3) a Model class containing the model structure and Patient Generator method. The model is run by creating an instance of Model and subsequently executing the run() method of that Model instance. To accomodate the need to conduct multiple runs, this is performed in a for loop.