Are Markov and semi-Markov models flexible enough for cognitive panel data?

Markov chains and semi-Markov models are standard tools used to describe the flow of subjects from health into various stages of a disease. Applications of these techniques face challenges when modeling the flow of elderly subjects through cognitive states into dementia due to the interval censoring of the entry into cognitive states, the transient nature of pre-dementia cognitive states, time-dependent risk factors, missing data, selection bias, and clinical diagnoses that may not agree with the gold standard diagnoses obtained at autopsy. There is a need to make these tools more flexible if they are to be used effectively when analyzing cognitive panel data.