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The homogeneous Markov assumption can fail because mortality and disease incidence depend on age, calendar time, and duration since stroke or dementia; duration dependence would call for a Semi-Markov multi-state model. The common-intensity assumption can fail through patient heterogeneity in sex, genetics, vascular risk, treatment, and frailty. In addition, clinic attendance may be informative: patients with worsening cognition may attend sooner or may be too ill to attend, violating an observation process independent of the latent disease process.

The model assumes a time-homogeneous Markov property: transition intensities depend only on the current state, not on calendar time, infection age, or earlier history. A Semi-Markov multi-state model could let recovery and death hazards depend on time since infection or entry into the current state. Weekly tests reveal recovery only by interval censoring, however, so the relevant entry and transition times are latent. Fitting the less restrictive model then requires integrating over unobserved paths, and the available data may contain too little information to identify a flexible duration-dependent hazard.