Research article

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

Hui-Chuan Shih¹ , Pesus Chou² , Chi-Ming Liu^2,3 and Tao-Hsin Tung^2,3,4

¹Department of Nursing, Kaohsiung Armed Forces General Hospital, Kaohsiung, Taiwan

²Community Medicine Research Center and Institute of Public Health, National Yang-Ming University, Taipei, Taiwan

³Department of Medical Research and Education, Cheng Hsin Rehabilitation Medical Center, Taipei, Taiwan

⁴Faculty of Public Health, School of Medicine, Fu-Jen Catholic University, Taipei, Taiwan

author email corresponding author email

BMC Medical Informatics and Decision Making 2007, 7:34doi:10.1186/1472-6947-7-34

Published:	9 November 2007

Abstract

Background

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.

Methods

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.

Results

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.

Conclusion

This approach may be useful in quantifying the progression of a variety of chronic diseases.