Title

線性轉換模型對不易感受性之區間設限資料分析

Translated Titles

Linear Transformation Model for Interval Censoring with a Cured Subgroup

DOI

10.6846/TKU.2012.00195

Authors

李明宣

Key Words

區間設限 ; 不易感受性 ; 轉換線性模型 ; EM演算法 ; Curemodel ; EMalgorithm ; Intervalcensoring ; Transformationmodel

PublicationName

淡江大學統計學系碩士班學位論文

Volume or Term/Year and Month of Publication

2012年

Academic Degree Category

碩士

Advisor

陳蔓樺

Content Language

繁體中文

Chinese Abstract

隨著生物臨床醫學研究的發展, 右設限資料分析方法在文獻中已廣為發展與應用, 區間設限資料為醫療研究收集過程中易遇到的資料, 例如病患的定期一段時間回診觀察, 未能確切知道發生事件的準確時間, 只觀察到某兩次回診時間區間中發生。 此外, 在資料結構上亦會收集到不易感受性的資料, 有些被觀察者在研究期間內不會發生我們所感興趣的事件, 通常被歸類為右設限資料, 但是這些資料為確切不發生之事件資料。 本篇考慮使用線性轉換模型(transformationmodel) 分析不易感受性區間設限資料, 使用EM 演算法(EM algorithm) 和牛頓迭代法(Newton-Raphsoniterationmethod) 估計參數, 並透過模擬驗證之。

English Abstract

There are numerous statistical methods reported for the analysis of right-censored failure time data in the past 30 years. In a medical follow-up study, additional problems arise in the analysis of interval censoring. For example, patients are observed periodically, we don't know the exact onset time of the disease, thus the observed failure time falls into a time period. In addition, we consider a data set with two populations. Some subjects (non-susceptibility) do not become events we are interested in and some subjects (susceptibility) become events we are interested in. The non-susceptible rate (cured rate) represents a combination of cure data and survival data. This thesis considers transformation model to analysis the interval censoring data with cured proportion. The EM algorithmis developed for the estimation and simulation studies are conducted.

Topic Category 基礎與應用科學 > 統計
管理學院 > 統計學系碩士班
Reference
  1. of transformation models with censored data. Biometrika, 89,
    連結:
  2. Efron, B. (1979). BootstrapMethods: Another Look at the Jackknife.
    連結:
  3. The Annals of Statistics, 7, pp.1-26.
    連結:
  4. Farewell VT. (1982). The use of mixture models for the analysis of
    連結:
  5. survival data with long-termsurvivors. Biometrics, 38, 1041-1046.
    連結:
  6. Finkelstein, D. M. and Wolfe, R. A. (1985). A semiparametricmodel
    連結:
  7. for regression analysis of interval-censored failure time data.
    連結:
  8. Biometrics, 41, 933-945.
    連結:
  9. Finkelstein, D.M. (1986). A proportional hazardmodel for interval-
    連結:
  10. Goedert, J., Kessler, C. Adedort, L. and et al. (1989). A prospective-
    連結:
  11. study of human immunodeficiency virus type-1 infection and the
    連結:
  12. development of AIDS in subjects with hemophilia. New England
    連結:
  13. Symposium in Biostatistics: Survival Analysis, eds. Lin, D, and
    連結:
  14. tional odds failure-time regressionmodelwith interval censoring.
    連結:
  15. Journal of the American Statistical Association, 92, 960-967.
    連結:
  16. Li, L. and Pu. Z. (1999). Regressionmodels with arbitrarily interval-
    連結:
  17. censored observations. Communications in Statistics. Theory
    連結:
  18. Lu, W. and Ying, Z. (2004). On semiparametric transformation cure
    連結:
  19. models. Biometrika, 91, 331-343.
    連結:
  20. Lam, K. F. and Xue, H. (2005). A semiparametric regression cure
    連結:
  21. Ma, S. (2009). Cure model with current status data. Statist. Sinica
    連結:
  22. Shen, X. (1998). Proportional odds regression and sieve maximum
    連結:
  23. Sun, J. (2006). The statistical analysis of interval-censored failure
    連結:
  24. time data. Springer, New York.
    連結:
  25. Taylor, J. M. G. (1995). Semiparametric estimation in failure time
    連結:
  26. comparing observed survival data to a standard population.
    連結:
  27. Biometrics, 37, 687-696.
    連結:
  28. estimation for semiparametric regression models with current
    連結:
  29. status data. Journal of the American Statistical Association, 99,
    連結:
  30. Yu, A. K. F., Kwan, K. Y. W., Chan, D. H. Y. and Fong, D. Y. T. (2001).
    連結:
  31. Clinical features of 46 eyes with calcified hydrogel intraocular
    連結:
  32. lenses. Journal of Cataract and Refractive Surgery, 27, 1596-1606.
    連結:
  33. of interval-censored failure time data with linear transformation
    連結:
  34. Banerjee, S. and Carlin, B. P. (2004). Parametric spatial cure rate
  35. models for interval-censored time-to-relapse data.Biometrics,
  36. 60, 268-275.
  37. Chen, K., Jin, Z. and Ying, Z. (2002). Semiparametric analysis
  38. 659-668.
  39. censored failure time data. Biometrics, 42, 845-854.
  40. Journal ofMedicine, 321, 1141-1148.
  41. Huang, J. and Wellner, J. A. (1997). Interval censored survival
  42. data: A review of recent progress. Proceedings of the First Seattle
  43. Fleming, T. Springer-Verlag, New York, 123-169.
  44. Huang, J. and Rossini, A. J. (1997). Sieve estimation for the propor-
  45. and Methods, 28, 1547-1563.
  46. model with current status data. Biometrika, 92, 573-586.
  47. 19, 233-249.
  48. Ma, S. (2010). Mixed case interval censored data with a cured
  49. subgroup, Statist. Sinica 20, 1165-1181.
  50. Rabinowitz, D., Tsiatis, A. A. and Aragon, J. (1995). Regression with
  51. interval-censored data. Biometrika, 82, 501-513.
  52. likelihood estimation. Biometrika, 85, 165-177.
  53. mixturemodels. Biometrics, 51, 899-907.
  54. Woolson, R. F. (1981) Rank tests and one-sample log rank test for
  55. Xue, H., Lam, K. F. and Li, G. (2004). Sevie maximum likelihood
  56. 346-356.
  57. Zhang, Z., Sun, L., Zhao, X. and Sun, J. (2005). Regression analysis
  58. models. The Canadian Journal of Statistics, 33, 61-70.
  59. 林建甫(2008) 。存活分析。雙葉書廊, 台北。
  60. 許靖涵博士。生醫影像專題系列:EM 演算法。