Should I use METER?¶
METER is specifically designed for discrete-time, time-inhomogenous, and acyclic multi-state markov models. To go through each of these elements individually:
The main assumption of a markov model is that the past states of a process do not affect transition probabilites going forward. If you have an application where the previous states of a process are likely to have an effect (if you are studying radiation exposure, for example) this assumption will not hold and you should consider using a semi-markov model.
Discrete-time means that observations are taken at regular time intervals that can be indexed by natural numbers. Typically this means that the data is indexed by days or years. This assumption will not hold for data with inconsistent intervals between observations, which would be typical for clinical data on cancer relapse and many types of survey data.
Acyclic means that states have a defined order and it is not possible to move backwards to a preceding state. For the time being, METER does not support cyclic models, although this is high on the list of planned improvements.
Time-inhomogenous means that time from study entry affects the probability of transitions between states. This is typical for data that stretches over long periods of time, where subject age may be a relevant factor. For example, an individual diagnosed with type 2 diabetes at age 20 is going to have a much better short-term survival prognosis than an 80-year-old with the same diagnosis.
You can still use METER if you believe that your process is time-homogenous, but you might have simpler options available. If you want to model a continuous time process, I suggest using the R package MSM.
If you haven’t used Python before I would recommend going through a tutorial or two and familiarizing yourself with basic datatypes, but you will not need any advanced knowledge of the language to use METER. You should also make sure that you know how to import .xls and .csv files as pandas dataframes. If you want to do anything beyond the core functionality of the package a decent background in pandas will be very useful.