English  |  正體中文  |  简体中文  |  Post-Print筆數 : 11 |  Items with full text/Total items : 88531/118073 (75%)
Visitors : 23457096      Online Users : 99
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    政大機構典藏 > 理學院 > 應用數學系 > 期刊論文 >  Item 140.119/111279
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/111279


    Title: Modeling chronic hepatitis B virus infections with survival probability metrics
    Authors: Chen, Jeng-Huei
    陳政輝
    Luh, Hsing Paul
    陸行
    Chen, Shin-Yu
    Chien, Rong-Nan
    Contributors: 應用數學系
    Keywords: Markov chain;Disease progression;A life table;Mean hitting time;Survival probability
    Date: 2017-03
    Issue Date: 2017-07-20 16:56:05 (UTC+8)
    Abstract: 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 of transitions are affected by the imposed population mortality. From realistic situations, no matter what state transitions patients experience, they are all under the risk of death caused by population mortality. Based on this observation, a new modeling approach is proposed in this study. The proposed approach assumes that population mortality independently affects all state transitions of a Markov model. This extends Beck and Pauker's idea and makes their method more reasonable. The proposed approach is applied to disease progression analysis of chronic hepatitis B virus (HBV) infection and considered for further applications. Specifically, with the natural history of chronic HBV infection originally described by a coarse Markov model a new model is developed by calibrating the coarse model with the proposed method. Numerical results show that the new model can obtain realistic estimates to patients’ life expectancy and survival probabilities. Meanwhile, the concept of first hitting time can find its interesting application in deriving the probabilities for patients’ first experiencing critical medical status during a specified duration. This delivers valuable information to chronic HBV patients. The model's possible extensions such as its application to different countries and taking patients’ risk factors into account are also discussed for future study.
    Relation: Operations Research for Health Care, 12, 29-42
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1016/j.orhc.2017.01.001
    DOI: 10.1016/j.orhc.2017.01.001
    Appears in Collections:[應用數學系] 期刊論文

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML372View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback