Estimation of progression of multi-state chronic disease using the Markov model and prevalence pool concept

<p>Abstract</p> <p>Background</p> <p>We propose a simple new method for estimating progression of a chronic disease with multi-state properties by unifying the prevalence pool concept with the Markov process model.</p> <p>Methods</p> <p>Estimation of progression rates in the multi-state model is performed using the E-M algorithm. This approach is applied to data on Type 2 diabetes screening.</p> <p>Results</p> <p>Good convergence of estimations is demonstrated. In contrast to previous Markov models, the major advantage of our proposed method is that integrating the prevalence pool equation (that the numbers entering the prevalence pool is equal to the number leaving it) into the likelihood function not only simplifies the likelihood function but makes estimation of parameters stable.</p> <p>Conclusion</p> <p>This approach may be useful in quantifying the progression of a variety of chronic diseases.</p