Multi-State Markov Model: an Application to Liver Cirrhosis

Sudeep Khanna (), Gurprit Grover (), Divya Seth () and V. Sreenivas ()

Statistics in Transition new series, 2013, vol. 14, issue 3, 429-442

Abstract: The control and treatment of chronic diseases is a major public health challenge, particularly for patients suffering from liver disease. In this paper, we propose a frame to estimate survival and death probabilities of the patients suffering from liver cirrhosis and HCC in the presence of competing risks. Database of the admitted patients in a hospital in Delhi has been used for the study. A stochastic illness-death model has been developed describing two liver illness states (Cirrhosis and HCC) and two death states (death due to liver disease and death due to competing risk). Individuals in the study were observed for one year of life at any age xi. The survival and death probabilities of the individuals suffering from liver cirrhosis and HCC have been estimated using the method of maximum likelihood. The probability of staying in the cirrhotic state is estimated to be threefold higher than that of developing HCC (0.64/0.21) in one year of life. The probability of cirrhotic patient moving to HCC state is twice (0.21/0.11) the probability of dying due to liver disease. HCC being the severe stage, the probability of patient dying due to HCC is three times that of cirrhosis. Markov model proves to be a useful tool for analysis of chronic degenerative disease like liver cirrhosis. It can provide in-depth insight for both the researchers and policy makers to resolve complex problems related to liver cirrhosis with irreversible transitions.

Keywords: illness-death model; maximum likelihood; Cirrhosis; Hepatocellular Carcinoma (HCC) (search for similar items in EconPapers)
Date: 2013
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://index.stat.gov.pl/repec/files/csb/stintr/csb_stintr_v14_2013_i3_n6.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:csb:stintr:v:14:y:2013:i:3:p:429-442

Access Statistics for this article

Statistics in Transition new series is currently edited by Włodzimierz Okrasa

More articles in Statistics in Transition new series from Główny Urząd Statystyczny (Polska) Contact information at EDIRC.
Bibliographic data for series maintained by Beata Witek ( this e-mail address is bad, please contact ).