I'm currently doing some transitional modelling using a first order markov transitional model to see how patients transition between 2 pre-defined states e.g. Disability and No-disability. I understand that with a markov transitional model, the transition from one state to another at ANY time point (say T) is only dependent upon the previous state (T-1). The model therefore includes a variable that adjusts for a patients previous state.
I've fitted the model for this and obtained the odds ratio (OR) for the transition at the time point of interest; this is fine and can be interpretted. As i mentioned before, the model adjusts for a patients previous state and this variable also has an (OR) in the model output however i am not sure how to interpret this odds ratio? Anyone know how this is interpretted?