A comparison of non-homogeneous Markov regression models with application to Alzheimer's disease progression.

@article{Hubbard2011ACO,
  title={A comparison of non-homogeneous Markov regression models with application to Alzheimer's disease progression.},
  author={Rebecca A. Hubbard and Xiao Hu Zhou},
  journal={Journal of applied statistics},
  year={2011},
  volume={38 10},
  pages={
          2313-2326
        }
}
Markov regression models are useful tools for estimating the impact of risk factors on rates of transition between multiple disease states. Alzheimer's disease (AD) is an example of a multi-state disease process in which great interest lies in identifying risk factors for transition. In this context, non-homogeneous models are required because transition rates change as subjects age. In this report we propose a non-homogeneous Markov regression model that allows for reversible and recurrent… CONTINUE READING