Thesis (Ph.D.) - Cairo University - Institute of Statistical Studies and Research - Department of Demography and Biostatistics

Multistate Markov models are well - established methods for estimating rates of transition between stages of any chronic diseases. The objective of this study is to propose a stochastic model that describes the progression process of chronic kidney disease (CKD), to estimate the mean time spent in each stage of the disease that precedes developing end-stage renal failure and to estimate the life expectancy of CKD patients. Continuous - time markov chain (CTMC) is an appropriate model of CKD. Explicit expressions of transition probability functions are derived by solving system of forward Kolmogorov differential equations. Besides, the mean sojourn time, the state probability distribution, life expectancy of a CKD patient and expected number of patients in each state of the system are estimated in the study. The model was fitted to a case-study sample of patients. The maximum likelihood estimates of the transition rates between successive stages of CKD are computed using numerical methods. Afterwards, the intensities of transition are estimated according to different characteristics of patients (demographic, socio-economic and medical

Issued also as CD