  This book handles the fatty liver disease from the bio-statistical point of
view . It discusses the disease process in the simple general form of
health-disease-death multi-states model . Continuous Time Markov Chains are
used to estimate the rate transition matrix utilizing the MLE and Quasi-Newton
formula , once obtained , the probability transition matrix can be estimated by
exponentiation of the rate matrix . The probability transition matrix can also
be obtained by solving the forward Kolmogorov differential equations , which
yields more stable solution than exponentiation of rate matrix. The disease
process is expanded in 9 states model to explain the transition among the
detailed stages of the disease process , in more elaborate form. The
probability transition matrix is used to estimate the number of patients in
each stage , this matrix along with the rate transition matrix , both are used
to estimate life expectancy of patients is each stage. These statistical
indices are of great value as they can help the health policy makers and
medical insurance managers to allocate the resources for investigating and
treating patients in different stages of the disease . This method is of a high
potential value to be used in longitudinal studies conducted by the
pharmaceutical companies to evaluate the effect of anti-fibrotic drugs used to
treat patients within the initial stages of fibrosis . Poisson regression model
is also used to relate the high risk covariates such as type 2 diabetes,
hypercholesterolemia , obesity and hypertension with the rate of progression
and evolution of the stages of the disease over time. The general model , the
expanded model and the model with covariates are illustrated by artificial
hypothetical examples to demonstrate the mathematical statistical indices .
