Modeling chronic hepatitis B virus infections with survival probability metrics

Publication date: Available online 18 January 2017 Source:Operations Research for Health Care Author(s): Jeng-Huei Chen, Shin-Yu Chen, Hsing Paul Luh, Rong-Nan Chien Progressions of chronic diseases can be modeled as Markov processes. Frequently, the model parameters are concluded based on distinct short-term clinical studies because of the difficulty of observing the entire progression process in one clinical study. Though this piece-by-piece approach provides a global picture to the disease progression process, it could lead to unrealistic results under in-depth analysis. For instance, without careful calibration, patients’ life expectancy computed from the model might be longer than that of the general population. Such results usually arise from that the effect of population mortality is not sensible or not well included in these short-term clinical studies. For chronic diseases with which patients may experience a long chain of successive states, this inaccuracy is more obvious. Beck and Pauker propose that the population mortality may be integrated into a disease progression model in their work. Their method provides a solution to the aforementioned difficulty. However, their approach to integrate the population mortality into the model implicitly assumes that the population mortality solely affects the transition probabilities for transitions to the death state and for self-transitions remaining in the initial states. They do not explain why only these two types...

Source: Operations Research for Health Care - January 18, 2017 Category: Hospital Management Source Type: research