博碩士論文 105225006 詳細資訊




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姓名 吳柏宏(Bo-Hong Wu)  查詢紙本館藏   畢業系所 統計研究所
論文名稱
(The Weibull joint frailty-copula model for meta-analysis with semi-competing risks data)
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摘要(中) 本篇論文中,我們關注的是二元(bivariate)半競爭風險資料(semi-competing risks)的統合分析(meta-analysis),其中一個事件時間是另一個事件時間的競爭風險。統合分析是收集來自不同獨立研究的資料並總結研究結果的一種統計方法。Rondeau等人(2015)提出了半競爭風險資料統合分析的joint frailty model,其中frailty應用於不同研究之間的異質性(heterogeneity)。他們提出的模型是Cox比例風險模型的延伸,其中加了兩個事件時間之間的條件獨立假設。Emura等人(2017a)利用Copula函數來放寬joint frailty model的條件獨立性假設,並提出了joint frailty-copula model。在joint frailty-copula model中的基線風險函數(baseline hazard function)是透過nonparametric method估計的。在本論文中,我們提出基線風險函數的Weibull分佈和joint frailty-copula model中frailty的Gamma分佈。我們證明Weibull model構成了Gamma frailty分佈的共軛模型,Weibull model給出了邊際動差,生存函數,分位數和平均剩餘壽命的明確表達式。這些數學特性不可能在spline models下推導出來。在點估計部分,使用最大概似估計法來估計模型中的未知參數,並開發其計算程式。在區間估計的部分,提出了兩種不同的構建標準誤和信賴區間的方法,並比較了它們的表現。我們進行模擬研究來驗證所提出方法的準確性。最後,我們使用卵巢癌患者資料來說明所提出的方法。
摘要(英) In this thesis, we are concerned about the meta-analysis of bivariate semi-competing risks data, where one event time is a competing risk for another event time. Meta-analysis is a statistical method that collects data from different independent studies and concludes research results. Rondeau et al. (2015) proposed the joint frailty model for the meta-analysis of semi-competing risks data, where frailty is applied for heterogeneity between different studies. The model they proposed is an extension of the Cox proportional hazard model, in which the assumption of conditional independence between two event times given frailty is imposed. Emura et al. (2017a) used the copula function to relax the conditional independence assumption of the joint frailty model, and proposed the joint frailty-copula model. The baseline hazard functions in the joint frailty-copula model are estimated non-parametrically by splines. In this thesis, we propose the Weibull distribution for baseline hazard functions and the gamma distribution for frailty in the joint frailty-copula model. We show that the Weibull model constitutes a conjugate model for the gamma frailty distribution, and that the Weibull models give explicit expressions for the marginal moments, survival functions, quantiles, and mean residual lifetimes. These mathematical properties are not possible to derive under the spline models. In the point estimation part, the maximum likelihood estimation method is used to estimate the unknown parameters in the model, and its computer programs are developed. In the part of the interval estimation, two different methods for constructing the standard error and confidence intervals are proposed and their performances are compared. We conduct simulation studies to examine the accuracy of the proposed methods. Finally, we use an ovarian cancer patient data to illustrate the proposed method.
關鍵字(中) ★ 二元生存模型
★ 伽瑪脆弱模型
★ 分層模型
★ 最大概似估計
★ 平均剩餘時間
★ 隨機效應
★ 存活分析
關鍵字(英) ★ Bivariate survival model
★ Gamma frailty
★ Hierarchical model
★ Maximum likelihood estimation
★ Mean residual life
★ Random effects
★ Survival analysis
論文目次 Contents
摘要 ...........................................................................................................................................i
Abstract ......................................................................................................................................ii
誌謝辭 .....................................................................................................................................iii
Contents ....................................................................................................................................iv
Chapter 1 Introduction ................................................................................................................1
Chapter 2 Review .......................................................................................................................3
2.1 Univariate Weibull distribution ..................................................................................3
2.2 Review of copulas .......................................................................................................6
Chapter 3 Joint models for meta-analysis ...................................................................................8
3.1 Meta-analytic data .......................................................................................................8
3.2 The joint frailty-copula model .....................................................................................8
3.3 The Weibull joint frailty-copula model .....................................................................10
3.4 Properties ..................................................................................................................10
Chapter 4 Estimation procedure ...............................................................................................19
4.1 Semi-competing risks data ........................................................................................19
4.2 Maximum likelihood inference .................................................................................21
4.3 Interval estimation ....................................................................................................23
Chapter 5 Computation .............................................................................................................26
Chapter 6 Simulation ................................................................................................................28
6.1 Simulation design .....................................................................................................28
6.2 Simulation results .....................................................................................................29
Chapter 7 Data analysis ............................................................................................................33
7.1 The ovarian cancer data .............................................................................................33
7.2 Variable selection .....................................................................................................38
7.3 Fitted results ..............................................................................................................41
7.4 Prognostic prediction.................................................................................................42
Chapter 8 Conclusion and discussion .......................................................................................48
Appendix A ..............................................................................................................................50
Appendix B ..............................................................................................................................63
Appendix C ..............................................................................................................................79
References ................................................................................................................................83
參考文獻 Belaghi RA, Asl MN (2016). Estimation based on progressively type-I hybrid censored data from the Burr XII distribution. Statistical Papers, 1-43. DOI: 10.1007/s00362-016-0849-5
Burr IW (1942). Cumulative frequency functions. The Annals of mathematical statistics, 13(2), 215-232.
Ha ID, Christian NJ, Jeong JH, Park J, Lee Y (2016). Analysis of clustered competing risks data using subdistribution hazard models with multivariate frailties. Statistical methods in medical research, 25(6), 2488-2505.
Emura T, Chen YH (2018). Analysis of Survival Data with Dependent Censoring: Copula-Based Approaches. Springer.
Emura T, Chen YH, Chen HY (2012). Survival prediction based on compound covariate under Cox proportional hazard models. PLoS One, 7(10), e47627.
Emura T, Chen YH (2016). Gene selection for survival data under dependent censoring: a copula-based approach. Statistical Methods in Medical Research, 25(6), 2840-2857.
Emura T, Matsui S, Chen HY (2018-). compound.Cox: univariate feature selection and compound covariate for predicting survival, Computer Methods and Programs in Biomedicine, in press.
Emura T, Nakatochi M, Murotani K, Rondeau V (2017a). A joint frailty-copula model between tumour progression and death for meta-analysis. Statistical methods in medical research, 26(6), 2649-2666.
Emura T, Nakatochi M, Matsui S, Michimae H., Rondeau V. (2017b). Personalized dynamic prediction of death according to tumour progression and high-dimensional genetic factors: meta-analysis with a joint model. Statistical methods in medical research, 0962280216688032. DOI: 10.1177/0962280216688032
Fine JP, Jiang H, Chappell R (2001). On semi-competing risks data. Biometrika, 88(4), 907-919.
Frechet M (1927). Sur la loi de probabilite de l′ecart maximum. Ann. Soc. Math. Polon., 6, 93-116.
Ganzfried BF, Riester M, Haibe-Kains B, et al (2013). CuratedOvarianData: clinically annotated data for the ovarian cancer transcriptome. Database, 2013.
Hall WJ, Wellner J (1984) Mean residual life. In: Csorg?oM,DawsonDA, Rao JNK, SalehAKME(eds) Proceedings of the international symposium on statistics and related topics. North-Holland, Amsterdam, pp 169–184.
Lai CD, Zhang L, Xie M (2004). Mean residual life and other properties of Weibull related bathtub shape failure rate distributions. International Journal of Reliability, Quality and Safety Engineering, 11(02), 113-132.
Li Z, Chinchilli VM, Wang M (2018) A Bayesian joint model of recurrent events and a terminal event. Biometrical Journal, to appear.
McLain AC, Ghosh SK (2011). Nonparametric estimation of the conditional mean residual life function with censored data. Lifetime data analysis, 17(4), 514-532.
Molenberghs G, Verbeke G, Efendi A, Braekers R, Demetrio CG (2015). A combined gamma frailty and normal random-effects model for repeated, overdispersed time-to-event data. Statistical methods in medical research, 24(4), 434-452.
Nelsen RB (2006). An introduction to copulas, 2nd. New York: SpringerScience Business Media.
Peng M, Xiang L, Wang S (2018). Semiparametric regression analysis of clustered survival data with semi-competing risks. Computational Statistics & Data Analysis, 124, 53-70.
Rondeau V, Gonzalez JR (2005). Frailtypack: a computer program for the analysis of correlated failure time data using penalized likelihood estimation. Computer methods and programs in biomedicine, 80(2), 154-164.
Rondeau V, Pignon JP, Michiels S, MACH-NC collaborative Group (2015). A joint model for the dependence between clustered times to tumour progression and deaths: A meta-analysis of chemotherapy in head and neck cancer. Statistical methods in medical research, 24(6), 711-729.
Rosin P, Rammler E. (1933) The Laws Governing the Fineness of Powdered Coal. Journal of the Institute of Fuel, 7: 29-36.
Weibull W (1951). Wide applicability. Journal of applied mechanics, 103(730), 293-297.
指導教授 江村剛志(Takeshi Emura) 審核日期 2018-8-6
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