Prognostic Factors for Evolution of Non Alcoholic Fatty Liver Disease Patients Utilizing Poisson Regression and Continuous Time Markov Chains

In the present paper, the deleterious effects of obesity, type 2diabetes and insulin resistance, systolic and diastolic hypertension on the rate of progression of fibrosis in non-alcoholic fatty liver disease (NAFLD) patients are illustrated using a new approach utilizing the Poisson regression to model the transition rate matrix. The observed counts in the transition counts matrix are used as response variables and the covariates are the risk factors for fatty liver. Then the estimated counts from running the Poisson regression are used to estimate the transition rates using the continuous time Markov chains (CTMC) followed by exponentiation of the estimated rate matrix to obtain the transition probability matrix at specific time points. Using a hypothetical data of 150 participants followed up every year for a total of 28 years recording their demographic characteristics and their timeline of follow up are demonstrated. The findings revealed that insulin resistance expressed by MOMA-IR 2 has the most deleterious effects among other factors for increasing the rate of forward progression of patients from state 1 to state 2 as well as from state 2 to state 3 and from state 3 to state 4. The higher the level of HOMA-IR is, the more rapid the rate of progression is.

Prognostic Factors for Evolution of Non Alcoholic Fatty Liver Disease Patients Utilizing Poisson Regression and Continuous Time Markov Chains

In the present paper, the deleterious effects of obesity, type 2diabetes and insulin resistance, systolic and diastolic hypertension on the rate of progression of fibrosis in non-alcoholic fatty liver disease (NAFLD) patients are illustrated using a new approach utilizing the Poisson regression to model the transition rate matrix. The observed counts in the transition counts matrix are used as response variables and the covariates are the risk factors for fatty liver. Then the estimated counts from running the Poisson regression are used to estimate the transition rates using the continuous time Markov chains (CTMC) followed by exponentiation of the estimated rate matrix to obtain the transition probability matrix at specific time points. Using a hypothetical data of 150 participants followed up every year for a total of 28 years recording their demographic characteristics and their timeline of follow up are demonstrated. The findings revealed that insulin resistance expressed by MOMA-IR 2 has the most deleterious effects among other factors for increasing the rate of forward progression of patients from state 1 to state 2 as well as from state 2 to state 3 and from state 3 to state 4. The higher the level of HOMA-IR is, the more rapid the rate of progression is.

Strong Stationary Duality and Algebraic Duality for Continuous Time Möbius Monotone Markov Chains

Under the assumption of Möbius monotonicity, we develop the theory of strong stationary duality for continuous time Markov chains on the finite partially ordered state space, we also construct a nonexplosive algebraic duality for continuous time Markov chains on Finally, we present an application to the two-dimensional birth and death chain.

On the forward algorithm for stopping problems on continuous-time Markov chains

We revisit the forward algorithm, developed by Irle, to characterize both the value function and the stopping set for a large class of optimal stopping problems on continuous-time Markov chains. Our objective is to renew interest in this constructive method by showing its usefulness in solving some constrained optimal stopping problems that have emerged recently.

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

describe fatty liver in its simplest form , health-illness-death process

Prognostic Factors for Evolution of Non Alcoholic Fatty Liver Disease Patients Utilizing Poisson Regression and Continuous Time Markov Chains

In this article , Poisson regression model is used to relates the rates of transition among states to the covariates .

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

describe fatty liver in its simplest form , health-illness-death process

New Method Utilizing Continuous Time Markov Chains to Analyze Evolution in the Nine States Model of Non-Alcoholic Fatty Liver Disease

it handles the disease in more extended multistate model

New Method Utilizing Continuous Time Markov Chains to Analyze Evolution in the Nine States Model of Non-Alcoholic Fatty Liver Disease

it handles the disease in more extended multistate model