跳到主要內容

簡易檢索 / 詳目顯示

研究生: 張雯涵
Wen-han Zhang
論文名稱: 以聯合模型探討原發性膽汁性肝硬化
Using Joint Model to Discuss PBC
指導教授: 曾議寬
Yi-kuan Tseng
口試委員:
學位類別: 碩士
Master
系所名稱: 理學院 - 統計研究所
Graduate Institute of Statistics
畢業學年度: 97
語文別: 中文
論文頁數: 65
中文關鍵詞: 聯合模型事件歷史圖
外文關鍵詞: joint model, event history plot
相關次數: 點閱:12下載:0
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 在臨床試驗或醫學研究中,普遍探討的問題是:『到底有哪些因素會影響疾病的發展 』或是 『藥物對病情的控制是否具有療效 』。
    解決這樣的問題可透過存活分析得到幫助,其中Cox比例風險模型最常被用來描述存活資訊與變數間的關係以便了解上述問題。
    這些影響疾病的變數常會隨時間而變動,我們稱此為長期追蹤資料。
    在追蹤過程中常會因某些因素導致資料不完整或是測量時有實驗誤差,這些都將影響Cox比例風險函數中參數的估計;
    在此,我們將採用隨機效應來描述變數的軌跡以解決上述問題,並以Cox比例風險函數與隨機效應所配適的聯合模型來進行實例分析,
    探討D-青黴胺藥物對於原發性膽汁性肝硬化(PBC)病人的存活時間的影響以及其膽紅素值的變化情況。
    我們初步地透過多種圖示法觀察,包括:事件歷史圖,等高圖以及3D平滑曲線圖。
    接著再進一步地使用聯合模型所得的估計值進行探討。
    最後根據這兩種方法我們得到相同的結論:第一,D-青黴胺藥物對於PBC病人的病情並沒有顯著的療效。
    第二,PBC病人的膽紅素值會與風險成正比,膽紅素值越高時,風險隨之上升。


    The main purpose of this thesis is to investigate the effect of D-penicillamine
    and bilirubin to lifetime of Primary Biliary Cirrhosis (PBC) patients simultaneously.
    Two methods are presented here, graphic techniques and
    joint-modeling approach. Graphic techniques are used to do preliminary
    exploring the data, which include Event history plot, Contour plot, and
    3D smoothing spline surfaces. The joint modeling approach is conducted
    to do statistical inferences on estimated parameters for the data. These
    two methods have led to the same conclusions. In particular, the drug
    D-penicillamine has no significant effect on survival. Moreover, the time
    dependent covariate bilirubin can be well described through a cubic random
    coefficient model and has a significant impact on patients’ lifetime.

    第一章緒論1 1.1 疾病介紹. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 模型背景介紹. . . . . . . . . . . . . . . . . . . . . . . . . . . 6 第二章統計方法13 2.1 圖形法. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.1.1 事件歷史圖. . . . . . . . . . . . . . . . . . . . . . . . 13 2.1.2 測量值的Profile 圖. . . . . . . . . . . . . . . . . . . 15 2.1.3 3D 平滑曲面圖. . . . . . . . . . . . . . . . . . . . . . 16 2.2 聯合模型. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 符號定義與模型介紹. . . . . . . . . . . . . . . . . . . 18 2.2.2 使用EM 演算法估計參數. . . . . . . . . . . . . . . . 21 2.2.3 估計參數之標準誤差. . . . . . . . . . . . . . . . . . . 26 第三章實例分析28 3.1 資料背景. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 iv 3.2 圖形法. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.2.1 事件歷史圖. . . . . . . . . . . . . . . . . . . . . . . . 32 3.2.2 Profile 圖. . . . . . . . . . . . . . . . . . . . . . . . 35 3.2.3 3D 平滑曲面圖. . . . . . . . . . . . . . . . . . . . . . 37 3.3 聯合模型. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 第四章結論與展望47 參考文獻52

    1. Cleveland, W.S. (1979) ”Robust Locally Weighted Regression and
    Smoothing Scatterplots” Journal of the American Statistical Association,
    Vol.74, No.368, pp.829-836.
    2. Cox, D.R. (1972) ”Regression Models and Life-Tables.” Journal of
    the Royal Statistical Society. Series B(Methodological), Vol.34, No.2,
    pp.187-220.
    3. Dickson, E.R., Grambsch, P.M., Fleming, T.R., Fisher, L.D. and Longworthy,
    N. (1989) ”Prognosis in Primary Biliary Cirrhosis: Model for
    Decision Making.” Hepatology, Vol.10, No.11, 1-7
    4. Dempster, A.P., Laird, N.M. and Rubin, D.B. (1977) ”Maximum Likelihood
    from Imcomplete Data via EM Algorithm” Journal of the Royal
    Statistical Society. Series B(Methodological), Vol.39, No.1, pp.1-38.
    5. Ding, J. and Wang, J.L. (2008) ”Modeling Longitudinal Data with
    Nonparametric Multiplicative Random Effects Jointly with Survival
    Data.” Biometrics, 64, 546-556
    6. Dubin, J.A., M¨uller, H.G. and Wang, J.L. (2001) ”Event history
    52
    graphs for censored survival data.” Statistics in Medicine, 20, 2951-
    2964
    7. Efron, B.(1979) ”Bootstrap methods: Another look at the jackknife.”
    Annual Statistician, Vol.7, No.7, pp.1-26.
    8. Fleming, T.R. and Harrington, D.P. (1991) ”Counting Processes and
    Survival Analysis.” New York: Weiley.
    9. Goldman, A.I. (1992) ”Eventcharts:Visualizing Survival and Other
    Timed-Events Data.” The American Statistician, Vol.46, No.1, pp.13-
    18
    10. Gong, Y., Klingenberg, S.L. and Gluud, C. (2004) ”D-penicillamine for
    primary biliary cirrhosis.” Cochrane Database of Systematic Reviews,
    New York: Wiley.
    11. Henderson, R., Diggle, P. and Dobson, A. (2000) ”Joint modelling of
    longitudinal measurements and event time data.” Biostatistics, Vol.1,
    No.4, pp.465-480
    12. Hsieh, F., Tseng, Y.K. and Wang, J.L. (2006) ”Joint Modeling of
    Survival and Longitudinal Data: Likelihood Approach Revisited.” Biometrics,
    62, 1037-1043
    53
    13. Lee, J.J., Hess, K.R. and Dubin, J.A. (2000) ”Extensions and Applications
    of Event Charts” The American Statistician, Vol.54, No.1,
    pp.63-70
    14. Markus, B.H., Dickson, E.R., Grambsch, P.M., Fleming, T.R., Mazzaferro,
    V., Klintmalm, G.B., Wiesner, R.H., Van Thiel, D.H., and
    Starzl, T.E. (1989) ”Efficiency of Liver Transplantation in Patients
    With Primary Biliary Cirrhosis” New England Journal of Medicine,
    Vol.320, 1709-1713
    15. Murtaugh, P.A., Dickson, E.R., Van Dam, G.M., Malinchoc, M., Grambsch,
    P.M., Langworthy, A.L. and Gips, C.H. (1994) ”Primary biliary
    cirrhosis: prediction of short-term survival based on repeated patient
    visits.” Hepatology, Vol.1 No.1, 126-134
    16. Neuberger, J., Christensen, E., Portmann, B., Caballeria, J., Rodes,
    J., Ranek, L., Tygstrup, N. and Williams, R.(1985) ”Double blind
    controlled trial of d-penicillamine in patients with primary biliary cirrhosis.”
    Gut 26,114-119
    17. Silverman, B.W. (1985) ”Spline Smoothing: The Equivalant Variable
    Kernel Method.” The Annals of Statistics, Vol.12, No.3, pp.898-916
    54
    18. Song, X., Davidian, M. and Tsiatis, A.A. (2002) ”A Semiparametric
    Likelihood Approach to Joint Modeling of Longitudinal and Time-to-
    Event Data.” Biometrics, 58, pp.742-753
    19. Taylor, J.M.G., Cumberland, W.G. and Sy, J.P. (1994) ”A Stochastic
    Model for Analysis of Longitudinal AIDS Data.” Journal of the
    American Statistical Association, Vol.89, No.427, pp.727-736
    20. Taylor, J.M.G., and Law, N. (1998) ”Does the Covariance Structure
    Matter in Longitudinal Modelling for the Prediction of Future CD4
    Counts?” Statistics in Medicine, 17, 2381-2394
    21. Tseng, Y.K., Hsieh, F. and Wang, J.L. (2005) ”Joint modelling of
    accelerated failure time and longitudinal data.” Biometrika, Vol.92,
    No.3, pp.587-603
    22. Tsiatis, A.A. and Davidian, M. (2004) ”Joint Modeling of Longitudinal
    and Time-to-Event Data: An Overview.” Statistica Sinica, 14,
    809-834
    23. Tsiatis, A.A., DeGruttola, V. and Wulfsohn, M.S. (1995) ”Modeling
    the Relationship of Survival to Longitudinal Data Measured with Error.
    Applications to Survival and CD4 Counts in Patients with AIDS”
    55
    Journal of the American Statistical Association, Vol.90, No.429, pp.27-
    37
    24. Wang, Y. and Taylor, J.M.G. (2001) ”Jointly Modeling Longitudinal
    and Event Time DataWith Application to Acquired Immunodeficiency
    Syndrome” Journal of the American Statistical Association, Vol.96,
    No.455, pp.895-905
    25. Wulfsohn, M.S. and Tsiatis, A.A. (1997) ”A Joint Model for Survival
    and Longitudinal DataMeasured with Error.” Biometrics, Vol.53,
    No.1, pp.330-339
    26. Yu, M., Law, N.J., Taylor, J.M.G. and Sandler, H.M. (2004) ”Joint
    Longitudinal-Survival-Cure Models and Their Application to Prostate
    Cancer.” Statistica Sinica, 14, pp.835-862
    27. Zeng, D. and Cai, J. (2005) ”Asymptotic Results for Maximum Likelihood
    Estimators in Joint Analysis of Repeated Measurements and
    Survival Time.” The Annals of Statistics Vol.33, No.5, pp.2132-2163

    QR CODE
    :::