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題名 以類馬可夫模式評估疾病風險
Evaluate Prognostic Risks by Semi-Markov Model
作者 林亞萱
Lin, Ya-Syuan
貢獻者 陸行
Luh, Hsing
林亞萱
Lin, Ya-Syuan
關鍵詞 類馬可夫模型
設限資料
評估風險
Semi-Markov
Censored data
Risk accessment
日期 2022
上傳時間 2-May-2022 15:02:34 (UTC+8)
摘要 本研究以部分設限資料之類馬可夫模型為基礎,並根據健保資料庫蒐集之數據直接驗證此模型之可行性。

根據研究顯示,類馬可夫模型可以在醫學研究中分析患者狀態轉移的過程,且醫學研究中時常存在病程不完整的情況,所以我們認為部分設限資料之類馬可夫模型非常適合被應用在醫學研究中。希望透過現有的健保資料庫數據,將模型與實際數據作結合,活化醫療數據和使用。

驗證結果顯示,此估計模型在資料完整情況下是相當好的估計方法; 而在設限情況下透過模型估計出來的轉移機率與實際轉移機率無太大差異,所以此估計模型確實可以用來估計我們所感興趣的轉移機率。並且,雖然資料完整未必在所有疾病都可準確估計,但可以看出整體趨勢往實際數值靠近。
This thesis is based on the semi-Markov models for partially censored data. Data from the National Health Insurance Research Database are used to evaluate the feasibility of the model.

According to the research, semi-Markov models can be used to analyze the process of state transitions of the patient. However, the patient history in medical research is sometimes incomplete. We evaluate the semi-Markov models for partially censored data and find it can greatly fit for medical research. Patient data is extracted from National Health Insurance Research Database, enhancing the sustainability of medical data and application.

The verification result shows that this model performs well when the data is complete. Meanwhile, the estimate of transition probability under the censored situation is nonsignificantly different compared to the case with complete information. We can conclude that this model is suitable to estimate the transition probability that we are interested in. Still, although the completeness of information may not always induce precise prediction of all risks, but the approximation by the model correctly reflects the trend.
參考文獻 [1] G. H. Weiss and M. Zelen. A semi-markov model for clinical trials. Journal of Applied
Probability, 2(2):269–285, 1965.
[2] B. W. Turnbull, B. W. Brown Jr, and M. Hu. Survivorship analysis of heart transplant data.
Journal of the American Statistical Association, 69(345):74–80, 1974.
[3] S. W. Lagakos. A stochastic model for censored-survival data in the presence of an
auxiliary variable. Biometrics, pages 551–559, 1976.
[4] S. W. Lagakos, C. J. Sommer, and M. Zelen. Semi-markov models for partially censored
data. Biometrika, 65(2):311–317, 1978.
[5] James R Broyles, Jeffery K Cochran, and Douglas C Montgomery. A statistical markov
chain approximation of transient hospital inpatient inventory. European Journal of
Operational Research, 207(3):1645–1657, 2010.
[6] Gordon J Taylor, Sally I McClean, and Peter H Millard. Using a continuous-time markov
model with poisson arrivals to describe the movements of geriatric patients. Applied
stochastic models and data analysis, 14(2):165–174, 1998.
[7] Chiying Wang, Sergio A Alvarez, Carolina Ruiz, and Majaz Moonis. Computational
modeling of sleep stage dynamics using weibull semi-markov chains. In HEALTHINF,
pages 122–130, 2013.
[8] Benoit Liquet, Jean-François Timsit, and Virginie Rondeau. Investigating hospital
heterogeneity with a multi-state frailty model: application to nosocomial pneumonia
disease in intensive care units. BMC medical research methodology, 12(1):1–14, 2012.
18
[9] Jean-François Coeurjolly, Moliere Nguile-Makao, Jean-François Timsit, and Benoit
Liquet. Attributable risk estimation for adjusted disability multistate models: application
to nosocomial infections. Biometrical journal, 54(5):600–616, 2012.
[10] C. C. Huang. Nonhomogeneous Markov Chains and Their Applications. Ph. D. thesis,
Iowa State University, 1977.
[11] A. Listwon and P. Saint-Pierre. Semimarkov: An R package for parametric estimation in
multi-state semi-markov models. Journal of Statistical Software, 66(6):784, 2015.
[12] A. Asanjarani, B. Liquet, and Y. Nazarathy. Estimation of semi-markov multi-state models:
a comparison of the sojourn times and transition intensities approaches. The International
Journal of Biostatistics, 2021. doi.org/10.1515/ijb-2020-0083
[13] J. E. Ruiz-Castro and R. Pérez-Ocón. A semi-markov model in biomedical studies. 2004.
[14] M. J. Phelan. Estimating the transition probabilities from censored markov renewal
processes. Statistics & probability letters, 10(1):43–47, 1990.
[15] E. L. Kaplan and P. Meier. Nonparametric estimation from incomplete observations.
Journal of the American statistical association, 53(282):457–481, 1958.
描述 碩士
國立政治大學
應用數學系
107751011
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107751011
資料類型 thesis
dc.contributor.advisor 陸行zh_TW
dc.contributor.advisor Luh, Hsingen_US
dc.contributor.author (Authors) 林亞萱zh_TW
dc.contributor.author (Authors) Lin, Ya-Syuanen_US
dc.creator (作者) 林亞萱zh_TW
dc.creator (作者) Lin, Ya-Syuanen_US
dc.date (日期) 2022en_US
dc.date.accessioned 2-May-2022 15:02:34 (UTC+8)-
dc.date.available 2-May-2022 15:02:34 (UTC+8)-
dc.date.issued (上傳時間) 2-May-2022 15:02:34 (UTC+8)-
dc.identifier (Other Identifiers) G0107751011en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/139991-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 應用數學系zh_TW
dc.description (描述) 107751011zh_TW
dc.description.abstract (摘要) 本研究以部分設限資料之類馬可夫模型為基礎,並根據健保資料庫蒐集之數據直接驗證此模型之可行性。

根據研究顯示,類馬可夫模型可以在醫學研究中分析患者狀態轉移的過程,且醫學研究中時常存在病程不完整的情況,所以我們認為部分設限資料之類馬可夫模型非常適合被應用在醫學研究中。希望透過現有的健保資料庫數據,將模型與實際數據作結合,活化醫療數據和使用。

驗證結果顯示,此估計模型在資料完整情況下是相當好的估計方法; 而在設限情況下透過模型估計出來的轉移機率與實際轉移機率無太大差異,所以此估計模型確實可以用來估計我們所感興趣的轉移機率。並且,雖然資料完整未必在所有疾病都可準確估計,但可以看出整體趨勢往實際數值靠近。
zh_TW
dc.description.abstract (摘要) This thesis is based on the semi-Markov models for partially censored data. Data from the National Health Insurance Research Database are used to evaluate the feasibility of the model.

According to the research, semi-Markov models can be used to analyze the process of state transitions of the patient. However, the patient history in medical research is sometimes incomplete. We evaluate the semi-Markov models for partially censored data and find it can greatly fit for medical research. Patient data is extracted from National Health Insurance Research Database, enhancing the sustainability of medical data and application.

The verification result shows that this model performs well when the data is complete. Meanwhile, the estimate of transition probability under the censored situation is nonsignificantly different compared to the case with complete information. We can conclude that this model is suitable to estimate the transition probability that we are interested in. Still, although the completeness of information may not always induce precise prediction of all risks, but the approximation by the model correctly reflects the trend.
en_US
dc.description.tableofcontents 中文摘要............................................... i
Abstract..............................................ii
目錄................................................. iii
表目錄................................................ v
圖目錄................................................ vi
第一章 緒論............................................ 1
第一節 研究背景........................................ 1
第二節 研究動機........................................ 1
第三節 研究架構.........................................1

第二章 文獻探討........................................ 3
第一節 馬可夫模型文獻探討............................... 3
第二節 馬可夫模型與類馬可夫模型之差異.................... 3
第三節 部分設限資料之類馬可夫模型........................ 4

第三章 部分設限資料之類馬可夫模型....................... 5
第一節 模型定義........................................ 5
第二節 無母數最大概似估計法............................. 6
第三節 θˆ(i, j) 與 Qˆ(vk; i, j) 之變異數............... 8

第四章 研究與實踐..................................... 10
第一節 資料來源....................................... 10
第二節 數據整理....................................... 11
第三節 核對理論和數據................................. 12
一、pijk、pˆijk...................................... 12
二、θ(i, j; vk) 、θˆ(i, j, vk)....................... 12
三、θ(i, j) 、θˆ(i, j) .............................. 12
四、Q(t; i, j)、Qˆ(t; i, j).......................... 12
五、var{θˆ(i, j)}、var{Qˆ(vk; i, j)}................ 12
第四節 設限情況下之數據比較............................ 13

第五章 研究結論與建議................................. 16
第一節 研究結論....................................... 16
第二節 未來研究方向................................... 17

參考文獻............................................. 18
附錄 A 相關名詞定義................................... 20
A.1 類馬可夫模型定義.................................. 20
A.2 設限觀察值的馬可夫更新過程之定義................... 20
附錄 B 數據整理表格................................... 21
zh_TW
dc.format.extent 16450395 bytes-
dc.format.mimetype application/pdf-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107751011en_US
dc.subject (關鍵詞) 類馬可夫模型zh_TW
dc.subject (關鍵詞) 設限資料zh_TW
dc.subject (關鍵詞) 評估風險zh_TW
dc.subject (關鍵詞) Semi-Markoven_US
dc.subject (關鍵詞) Censored dataen_US
dc.subject (關鍵詞) Risk accessmenten_US
dc.title (題名) 以類馬可夫模式評估疾病風險zh_TW
dc.title (題名) Evaluate Prognostic Risks by Semi-Markov Modelen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) [1] G. H. Weiss and M. Zelen. A semi-markov model for clinical trials. Journal of Applied
Probability, 2(2):269–285, 1965.
[2] B. W. Turnbull, B. W. Brown Jr, and M. Hu. Survivorship analysis of heart transplant data.
Journal of the American Statistical Association, 69(345):74–80, 1974.
[3] S. W. Lagakos. A stochastic model for censored-survival data in the presence of an
auxiliary variable. Biometrics, pages 551–559, 1976.
[4] S. W. Lagakos, C. J. Sommer, and M. Zelen. Semi-markov models for partially censored
data. Biometrika, 65(2):311–317, 1978.
[5] James R Broyles, Jeffery K Cochran, and Douglas C Montgomery. A statistical markov
chain approximation of transient hospital inpatient inventory. European Journal of
Operational Research, 207(3):1645–1657, 2010.
[6] Gordon J Taylor, Sally I McClean, and Peter H Millard. Using a continuous-time markov
model with poisson arrivals to describe the movements of geriatric patients. Applied
stochastic models and data analysis, 14(2):165–174, 1998.
[7] Chiying Wang, Sergio A Alvarez, Carolina Ruiz, and Majaz Moonis. Computational
modeling of sleep stage dynamics using weibull semi-markov chains. In HEALTHINF,
pages 122–130, 2013.
[8] Benoit Liquet, Jean-François Timsit, and Virginie Rondeau. Investigating hospital
heterogeneity with a multi-state frailty model: application to nosocomial pneumonia
disease in intensive care units. BMC medical research methodology, 12(1):1–14, 2012.
18
[9] Jean-François Coeurjolly, Moliere Nguile-Makao, Jean-François Timsit, and Benoit
Liquet. Attributable risk estimation for adjusted disability multistate models: application
to nosocomial infections. Biometrical journal, 54(5):600–616, 2012.
[10] C. C. Huang. Nonhomogeneous Markov Chains and Their Applications. Ph. D. thesis,
Iowa State University, 1977.
[11] A. Listwon and P. Saint-Pierre. Semimarkov: An R package for parametric estimation in
multi-state semi-markov models. Journal of Statistical Software, 66(6):784, 2015.
[12] A. Asanjarani, B. Liquet, and Y. Nazarathy. Estimation of semi-markov multi-state models:
a comparison of the sojourn times and transition intensities approaches. The International
Journal of Biostatistics, 2021. doi.org/10.1515/ijb-2020-0083
[13] J. E. Ruiz-Castro and R. Pérez-Ocón. A semi-markov model in biomedical studies. 2004.
[14] M. J. Phelan. Estimating the transition probabilities from censored markov renewal
processes. Statistics & probability letters, 10(1):43–47, 1990.
[15] E. L. Kaplan and P. Meier. Nonparametric estimation from incomplete observations.
Journal of the American statistical association, 53(282):457–481, 1958.
zh_TW
dc.identifier.doi (DOI) 10.6814/NCCU202200399en_US