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請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79235
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dc.contributor.advisor黃彥棕(Yen-Tsung Huang)
dc.contributor.authorYun-Lin Hoen
dc.contributor.author何昀霖zh_TW
dc.date.accessioned2022-11-23T08:56:20Z-
dc.date.available2022-02-21
dc.date.available2022-11-23T08:56:20Z-
dc.date.copyright2022-02-21
dc.date.issued2022
dc.date.submitted2022-01-24
dc.identifier.citationReferences [1] O. O. Aalen, R. J. Cook, and K. Roysland. Does Cox analysis of a randomized survival study yield a causal treatment effect? Lifetime Data Analysis, 21(4): 579-593.2015. [2] P. K. Andersen and R. D. Gill. Cox's regression model for counting processes: a large sample study. The annals of statistics, pages 1100-1120, 1982. [3] R. M. Baron and D. A. Kenny. The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations Journal of personality and social psychology, 51(6): 1173-1182, 1986. [4] Y. Bilias, M. Gu, and Z. Ying. Towards a general asymptotic theory for Cox model with staggered entry. The Annals of Statistics, 25(2): 662-682, 1997. [5] N. E. Breslow. Discussion of the paper by D. R. Cox. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 34(2): 216-217, 1972 [6] C. J. Chen, H. I. Yang, J. Su, C. L. Jen, S. L. You, S. N. Lu, G. T Huang, U. H Tloeje, and R.-H. S. Group. Risk of hepatocellular carcinoma across a biological gradient of serum hepatitis B virus DNA level. The Journal of the American Medical Association,295(1):65-73,2006. [7] S. H. Cho and Y. T. Huang. Mediation analysis with causally ordered mediators using Cox proportional hazards model. Statistics in medicine, 38(9): 1566-1581, 2019. [8] D. G. Clayton. A model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence Biometrika,65(1):141-151,1978. [9] D. R. Cox. Regression models and life-tables. Journal of the Royal Statistical Society: Series B(Methodological), 34(2): 187-202, 1972. [10] D. R. Cox. Partial likelihood. Biometrika, 62(2): 269-276, 1975. [11] R. Day, J. Bryant, and M. Lefkopoulou. Adaptation of bivariate frailty models for prediction, with application to biological markers as prognostic indicators. Biometrika,84(1):45-56,1997. [12] J. P. Fine, H. Jiang, and R. Chappell. On semi-competing risks data. Biometrika,88(4):907-919,2001. [13] J. S. Hong, S. H. Cho, and Y. T. Huang Semiparametric causal mediation modeling of hepatitis on mortality through liver cancer incidence. 2022+. [14 Y. T. Huang Joint significance tests for mediation effects of socioeconomic adversity on adiposity via epigenetics. The Annals of Applied Statistics, 12(3): 1535-1557,2018. [15] Y. T. Huang. Genome-wide analyses of sparse mediation effects under composite null hypotheses. The Annals of Applied Statistics, 13(1): 60-84, 2019. [16] Y. T. Huang. Causal mediation of semi-competing risks Biometrics, 2021. [17] Y. T. Huang and T. Cai. Mediation analysis for survival data using semiparametric probit models. Biometrics, 72(2): 563-574, 2016 [18] Y. T. Huang, C. L. Jen, H. I. Yang, M. H Lee, J. Su, S N. Lu, U. H. Iloeje, and C. J. Chen. Lifetime risk and sex difference of hepatocellular carcinoma among patients with chronic hepatitis B and C. Journal of Clinical Oncology, 29(27): 3643-3650,2011 [19] Y. T. Huang and W. C. Pan. Hypothesis test of mediation effect in causal mediation model with high-dimensional continuous mediators. Biometrics, 72(2) 402-413,2016. [20] KImai, L. Keele, and T. Yamamoto. Identification, inference and sensitivity analysis for causal mediation effects. Statistical science, pages 51-71, 2010. [21] J. D. Kalbfleisch and R. L. Prentice. The statistical analysis of failure time data, volume 360. John Wiley Sons, 2011. [22] T. Lange and J. V. Hansen. Direct and indirect effects in a survival context Epidemiology, pages 575-581 2011. [23] D. Mackinnon. Introduction to statistical mediation analysis. Routledge, 2012. [24] D. P. Mackinnon, C. M. Lockwood, J. M. Hoffman, S. G. West, and V. Sheets. A comparison of methods to test mediation and other intervening variable effects. Psychol Methods, 7(1): 83-104, 2002. [25] D. Oakes. A model for association in bivariate survival data. Journal of the Royal Statistical Society Series B(Methodological), pages 414-422, 1982. [26] D. Oakes. Semiparametric inference in a model for association in bivariate survival data. Biometrika,73(2):353-361,1986. [27] D. Oakes. Bivariate survival models induced by frailties. Journal of the American Statistical Association, 84(406): 487-493, 1989. [28] J. Pearl. Direct and indirect effects. pages 411-420, 2001. [29] J. M. Robins. Semantics of causal DAG models and the identification of direct and indirect effects. 2003. [30] D. B. Rubin. Bayesian inference for causal effects: The role of randomization. The Annals of statistics, pages 34-58, 1978. [31] E. J. T. Tchetgen. On causal mediation analysis with a survival outcome. The international journal of biostatistics, 7(1): 1-38, 2011. [32] T. J. Vander Weele. Causal mediation analysis with survival data. Epidemiology (Cambridge, Mass.), 22(4): 582, 2011. [33] T. J. Vander Weele and S. Vansteelandt. Conceptual issues concerning mediation interventions and composition. Statistics and its Interface, 2(4) 457-468, 2009. [34] W. Wang. Estimating the association parameter for copula models under dependent censoring. Journal of the Royal Statistical Society: Series B (Statistical Methodology),65(1):257-273,2003. [35] W. Wang. Nonparametric estimation of the sojourn time distributions for a multipath model. Journal of the Royal Statistical Society: Series B(Statistical Methodology), 65(4):921-935,2003. [36] J. Xu, J. D. Kalbfleisch, and B. Tai. Statistical analysis of illness-death processes and semicompeting risks data. Biometrics, 66(3): 716-725, 2010. [37] D. Zeng and D. Y. Lin. Maximum likelihood estimation in semiparametric regression models with censored data. Journal of the Royal Statistical Society: Series B (Statistical Methodology ), 69(4): 507-564, 2007. [38] S. D. Zhao, T. T. Cai, and H. Li. More powerful genetic association testing via a new statistical framework for integrative genomics. Biometrics, 70(4): 881-890, 2014.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79235-
dc.description.abstract在因果中介模型中,由於中介事件(例如:肝硬化)可能會被主事件(例如:肝癌)設限(censored),所以半競爭風險之因果中介效應分析在藥學研究裡已經成為一個重要的議題。在因果中介效應分析中將暴露到主事件的效應拆解成間接與直接效應,其中分別表示暴露到主事件之效應有無通過中介因子。這此我們根據所提模型建立檢定程序用於檢查是否有中介效應(間接效應)的存在。在反事實結果(counterfactual outcome)架構下,我們用計次過程來定義因果中介效應,並提出交集-聯集檢定(IUT檢定)來評估是否有足夠的統計證據證明中介效應的存在,我們先使用一個Cox風險等比模型和一系列的邏輯式迴歸模型來建構中介效應,再利用模型中的半母數參數估計來建構IUT檢定的統計檢定量。為了和我們提出的IUT檢定做比較,我們將IUT檢定和加權對數秩檢定(WLR檢定)做連結,之後再推導出兩個統計檢定之統計檢定量的大樣本性質,最後證明IUT檢定的檢定規模為 alpha,而且比WLR檢定有更高的檢定力。在數值模擬中,我們證明即使有干擾機制的存在,依據模型所提出的IUT檢定和WLR檢定也可以加入干擾因子做校正,而且依然能夠守住型一誤差的比率。分析結果根據我們的方法指出:B肝病毒和C肝病毒皆會透過肝硬化影響得到肝癌的風險。zh_TW
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Previous issue date: 2022
en
dc.description.tableofcontentsVerification Letter from the Oral Examination Committee i Acknowledgements iii 摘要 v Abstract vii Contents ix List of Figures xiii List of Tables xv Chapter 1 Introduction 1 Chapter 2 Causal mediation model and mediation effect 5 Chapter 3 Intersection-union test 11 Chapter 4 Connection to another test statistic 15 Chapter 5 Asymptotic results 17 Chapter 6 Revisiting intersection-union test 21 Chapter 7 Simulation 25 Chapter 8 Application to empirical data 29 Chapter 9 Discussion 33 References 35 Appendix A-assumption and Proof 41 A.1 Assumption 41 A.2 Proof of Theorem 1 42 A.3 Proof of Theorem 2 44 A.4 Proof of Theorem 3 46 A.5 Proof of Theorem 4 46 A.6 Proof of Theorem 5 47 Appendix B-tables and figures in simulation 49 B.1 Type 1 null hypothesis 49 B.2 Type 1 null hypothesis in the presence of confounding without adjustment 53 B.3 Type 1 null hypothesis in the presence of confounding with adjustment 56 B.4 Type 2 null hypothesis 59 B.5 Type 2 null hypothesis in the presence of confounding without adjustment 62 B.6 Type 2 null hypothesis in the presence of confounding with adjustment 65 B.7 Alternative hypothesis 68
dc.language.isoen
dc.title半競爭風險下因果中介效應分析基於模型的假設檢定zh_TW
dc.titleModel-Based Hypothesis Tests for the Causal Mediation of Semi-Competing Risksen
dc.date.schoolyear110-1
dc.description.degree碩士
dc.contributor.oralexamcommittee王維菁(Chia-Yen Lee),林聖軒(Kwei-Long Huang),程毅豪
dc.subject.keyword因果中介模型,Cox風險等比模型,無母數最大概似估計,半競爭風險模型,交集-聯集檢定,加權對數秩檢定,zh_TW
dc.subject.keywordCausal mediation model,Cox proportional hazards model,Nonparametric maximum likelihood estimator,Semi-competing risks,Intersection-union test,Weighted log-rank test,en
dc.relation.page69
dc.identifier.doi10.6342/NTU202200127
dc.rights.note同意授權(全球公開)
dc.date.accepted2022-01-27
dc.contributor.author-college理學院zh_TW
dc.contributor.author-dept應用數學科學研究所zh_TW
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