Given our new implementation of the graph representation and particularly the Tumor node implementation, it should be straightforward to enable tracking a tumor's T-category as a random variable:
We basically need to give the Tumor node the allowed_states = [0, 1, 2, 3, 4] and add an Edge instance that has this Tumor node both as start and as end (similar to the growth edges). This edge would then be parametrized with a probability that during one time-step the T-category increases by one.
If then functions like the generate_transition() don't just iterate over LNLs, but over all nodes and consider their possible evolutions, this Tumor node's state would be automatically incorporated and tracked as a random variable, just like all other LymphNodeLevel node's states.
When tracking T-category as a random variable, the distributions over diagnose times make no sense anymore and we would need to define a "diagnosis probability" that depends on all the node's states. We discussed this idea already.
Given our new implementation of the graph representation and particularly the
Tumornode implementation, it should be straightforward to enable tracking a tumor's T-category as a random variable:We basically need to give the
Tumornode theallowed_states = [0, 1, 2, 3, 4]and add anEdgeinstance that has thisTumornode both as start and as end (similar to the growth edges). This edge would then be parametrized with a probability that during one time-step the T-category increases by one.If then functions like the
generate_transition()don't just iterate over LNLs, but over all nodes and consider their possible evolutions, thisTumornode's state would be automatically incorporated and tracked as a random variable, just like all otherLymphNodeLevelnode's states.When tracking T-category as a random variable, the distributions over diagnose times make no sense anymore and we would need to define a "diagnosis probability" that depends on all the node's states. We discussed this idea already.