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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 李文宗 | |
| dc.contributor.author | Christine Chen | en |
| dc.contributor.author | 陳雅文 | zh_TW |
| dc.date.accessioned | 2021-06-08T02:55:39Z | - |
| dc.date.copyright | 2017-08-28 | |
| dc.date.issued | 2017 | |
| dc.date.submitted | 2017-08-04 | |
| dc.identifier.citation | 1. Rothman KJ, Greenland S, Lash TL, eds. Modern epidemiology. 3rd ed. Philadelphia, PA: Lippincott Williams & Wilkins; 2008.
2. Bollen KA. Structural equations with latent variables. New York, NY: Wiley; 1989. 3. Kaplan D. Structural equation modeling: foundations and extensions. 2nd ed. Thousand Oaks, CA: Sage; 2009. 4. Kline RB. Principles and practice of structural equation modeling. 4th ed. New York, NY: Guilford; 2015. 5. Robins JM, Greenland S. Identifiability and exchangeability for direct and indirect effects. Epidemiology. 1992;3:143-155. 6. Pearl J. Direct and indirect effects. in Proceedings of the seventeenth conference on uncertainty in artificial intelligence. 2001. Morgan Kaufmann Publishers Inc. 7. VanderWeele TJ. A unification of mediation and interaction: a 4-way decomposition. Epidemiology. 2014;25:749-761. 8. Cole P, MacMahon B. Attributable risk percent in case-control studies. Brit J prev soc Med. 1971;25:242-244. 9. Miettinen OS. Proportion of disease caused or prevented by a given exposure, trait or intervention. Am J Epidemiol. 1974;99:325-332. 10. Walter S. The estimation and interpretation of attributable risk in health research. Biometrics. 1976;32:829-849. 11. Bruzzi P, Green SB, Byar DP, Brinton LA, Schairer C. Estimating the population attributable risk for multiple risk factors using case-control data. Am J Epidemiol. 1985;122:904-914. 12. Benichou J. A review of adjusted estimators of attributable risk. Stat Methods Med Res. 2001;10:195-216. 13. Liao SF, Lee WC. Weighing the causal pies in case-control studies. Ann Epidemiol. 2010;20:568-573. 14. Lee WC. Completion potentials of sufficient component causes. Epidemiology. 2012;23:446-453. 15. Eide GE, Gefeller O. Sequential and average attributable fractions as aids in the selection of preventive strategies. J Clin Epidemiol. 1995;48:645-655. 16. Land M, Gefeller O. A game‐theoretic approach to partitioning attributable risks in epidemiology. Biom J. 1997;39:777-792. 17. Land M, Vogel C, Gefeller O. Partitioning methods for multifactorial risk attribution. Stat Methods Med Res. 2001;10:217-230. 18. McElduff P, Attia J, Ewald B, Cockburn J, Heller R. Estimating the contribution of individual risk factors to disease in a person with more than one risk factor. J Clin Epidemiol. 2002;55:588-592. 19. Llorca J, Delgado-Rodrı́guez M. A new way to estimate the contribution of a risk factor in populations avoided nonadditivity. J Clin Epidemiol. 2004;57:479-483. 20. Rabe C, Lehnert-Batar A, Gefeller O. Generalized approaches to partitioning the attributable risk of interacting risk factors can remedy existing pitfalls. J Clin Epidemiol. 2007;60:461-468. 21. Rothman KJ. Causes. Am J Epidemiol. 1976;104:587-592. 22. Hafeman DM. A sufficient cause based approach to the assessment of mediation. Eur J Epidemiol. 2008;23:711-721. 23. Gatto NM, Campbell UB. Redundant causation from a sufficient cause perspective. Epidemiol Persp Innov. 2010;7:5. 24. Suzuki E, Yamamoto E, Tsuda T. On the relations between excess fraction, attributable fraction, and etiologic fraction. Am J Epidemiol. 2012;175:567-575. 25. Lee WC. Assessing causal mechanistic interactions: a peril ratio index of synergy based on multiplicativity. PLOS ONE. 2013;8:e67424. 26. Richiardi L, Bellocco R, Zugna D. Mediation analysis in epidemiology: methods, interpretation and bias. Int J Epidemiol. 2013;42:1511-1519. 27. Robins JM, Greenland S. Estimability and estimation of excess and etiologic fractions. Stat Med. 1989;8:845-859. 28. Vansteelandt S, VanderWeele TJ. Natural direct and indirect effects on the exposed: effect decomposition under weaker assumptions. Biometrics. 2012;68:1019-1027. 29. Tchetgen EJT, VanderWeele TJ. Identification of natural direct effects when a confounder of the mediator is directly affected by exposure. Epidemiology. 2014;25:282-291. 30. VanderWeele TJ, Vansteelandt S, Robins JM. Effect decomposition in the presence of an exposure-induced mediator-outcome confounder. Epidemiology. 2014;25:300-306. 31. Kalbfleisch J, Lawless JF. The analysis of panel data under a Markov assumption. J Am Stat Assoc. 1985;80:863-871. 32. Kay R. A Markov model for analysing cancer markers and disease states in survival studies. Biometrics. 1986;42:855-865. 33. Jackson CH. Multi-state models for panel data: the msm package for R. J Stat Softw 2011;38:1-28. 34. Welton NJ, Ades A. Estimation of Markov chain transition probabilities and rates from fully and partially observed data: uncertainty propagation, evidence synthesis, and model calibration. Med Decis Making. 2005;25:633-645. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/20619 | - |
| dc.description.abstract | 探究暴露和疾病的關係是流行病學的宗旨。研究者有時會對由「果」(疾病)溯「因」(暴露)有興趣,即所謂的「歸因」。然而,目前的歸因方法未能考慮罹病的途徑。本研究中,我們以因果圓派模式為基礎,提出一個方法將疾病歸因於多重罹病途徑。我們針對三種不同的目的進行疾病的歸因,分別是罹病途徑的歸因、介入計畫的評估和侵權行為責任的分配。我們以一範例資料呈現本研究方法,並附上簡單易用的程式。我們推薦本研究方法成為流行病學研究分析的常規方法。 | zh_TW |
| dc.description.abstract | Characterizing exposure-disease associations is the central tenet of epidemiology. Researchers may want to evaluate exposure-disease associations by assessing whether “outcomes” (diseases) are induced by “causes” (exposures), which is the so-called “attribution”. However, current methods for disease attribution did not take disease pathways into consideration. In this paper, we propose a method to attribute diseases to multiple pathways based on the causal-pie model. The proposed method can be used to attribute diseases to pathways, to evaluate the intervention strategies and to apportion responsibility in tort-law liability issues. Our method is illustrated by an example data and an easy-to-use code is provided. We recommend the present method for routine use during the analysis of the epidemiologic data. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T02:55:39Z (GMT). No. of bitstreams: 1 ntu-106-R04849010-1.pdf: 1626505 bytes, checksum: a0766c9ca7cb8acffda79b5200cd5b3a (MD5) Previous issue date: 2017 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 摘要 iii Abstract iv 圖目錄 vi 表目錄 vii 第一章 前言 1 第二章 方法 3 2.1中介變數和疾病的發生 3 2.2疾病歸因 4 第三章 範例 8 第四章 討論 10 參考文獻 18 附錄 21 | |
| dc.language.iso | zh-TW | |
| dc.title | 將疾病歸因於多重途徑:以因果圓派建模之方法 | zh_TW |
| dc.title | Attributing Diseases to Multiple Pathways: a Causal-Pie Modeling Approach | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 105-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 黃彥棕,蕭朱杏 | |
| dc.subject.keyword | 流行病學方法,歸因,罹病途徑,因果圓派模式, | zh_TW |
| dc.subject.keyword | epidemiologic methods,attribution,disease pathways,causal-pie model, | en |
| dc.relation.page | 31 | |
| dc.identifier.doi | 10.6342/NTU201702515 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2017-08-04 | |
| dc.contributor.author-college | 公共衛生學院 | zh_TW |
| dc.contributor.author-dept | 流行病學與預防醫學研究所 | zh_TW |
| 顯示於系所單位: | 流行病學與預防醫學研究所 | |
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| ntu-106-1.pdf 未授權公開取用 | 1.59 MB | Adobe PDF |
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