Continuous Time Markov Chains for Analysis of Non-Alcoholic Fatty liver Disease Evolution

TL;DR

This paper models the progression of non-alcoholic fatty liver disease using continuous time Markov chains, estimating transition rates and patient life expectancy, and introduces a novel MLE approach for handling missing data in longitudinal studies.

Contribution

It introduces a new application of MLE to estimate transition rates in CTMC models for NAFLD progression, addressing missing data issues.

Findings

Transition rates closely match observed data

Estimated patient life expectancy provided

Method effectively handles missing follow-up data

Abstract

In the present paper, progression of non-alcoholic fatty liver disease (NAFLD) process is modeled by Continuous time Markov chains (CTMC) with 4 states .The transition intensities among the states are estimated using maximum likelihood estimation (MLE) method. The transition probabilities are also calculated. The mean sojourn time and its variance are estimated as well as the state probability distribution and its asymptotic covariance matrix. The life expectancy of the patient, one of the important statistical indices, is also obtained. The paper illustrates the new approach of using MLE to compensate for missing values in the follow up periods of patients in the longitudinal studies. This new approach also yields that the estimated rates among states are approximately equals to the observed rates.