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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 張淑惠(Shu-Hui Chang) | |
dc.contributor.author | Li-Ching Wang | en |
dc.contributor.author | 王立欽 | zh_TW |
dc.date.accessioned | 2021-06-16T13:22:43Z | - |
dc.date.available | 2018-09-24 | |
dc.date.copyright | 2013-09-24 | |
dc.date.issued | 2013 | |
dc.date.submitted | 2013-07-24 | |
dc.identifier.citation | Chang, S-H.Analysis of recurrent gap times for clustered data.NSC 101-2118-M-002 -006 -,2012
Chang, S-H. Nonparametric estimation of survival function for clustered recurrent gap time data. Technique Report of Division of Biostatistics, College of Public Health, National Taiwan University, 2013, 1-15. Cui J, Forbes A,et.al . Semi-parametric risk prediction models for recurrent cardiovascular events in the LIPID study.BMC Medical Research Methodology 2010, 10-27. Follmann D, Proschan M, Leifer E. Multiple outputation: inference for complex clustered data by averaging analyses from independent data. Biometrics 2003, 59:420-29. Gail MH, Santner TJ, Brown CC. An analysis of comparative carcinogenesis experiments based on multiple times to tumor.Biometrics 1980, 36:255-266. Hoffman EB, Sen PK, Weinberg CR. Within-cluster resampling.Biometrika 2001, 61:439–47. Luo, X, and Huang, C-Y.Analysis of recurrent gap time data using the weighted risk-set method and the modified within-cluster resampling method.Statistic in medicine 2010, 30, 301-311. Schaubel D, Cai J. Semiparametric methods for clustered recurrent event data. Life time data Anal 2005, 11:405-425. Sorock GS, Quigley PA, Rutledge MK, Taylor J, Luo X, et al. Central nervous system medication changes and falls in nursing home residents. Geriatric Nursing 2009, 30:334-340. Wang, M-C, Chang, S-H.Nonparametric estimation of a recurrent survival function.Journal of the American Statistical Association 1999, 94,146-153. Williamson JM, Kim HY, et al. Modeling survival data with informative cluster size. Statistic in medicine 2008, 27:543-555. Xiang Guo, AnastasiosTsiatis. A Weighted Risk Set Estimator for Survival Distributions in Two-Stage Randomization Designs with Censored Survival Data,The International Journal of Biostatistics 2005, 1, 1, 1. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/62003 | - |
dc.description.abstract | 在追蹤研究中,針對某一感興趣事件記錄並蒐集個體重複此事件的相關資料稱為復發事件資料,而隨著各種研究的需求或資料的特性,將研究個體劃分於各群集中為常見處理個體間關聯性的分析方式,例如家族資料與多中心的研究需要分別以家庭和各個中心為群集,而結合復發事件資料與群集資料稱為群集復發資料,在此資料結構下兩相鄰復發事件的間隔時間通常是感興趣的分析對象,且同時存在相同群集內不同個體的相關性與同一個體的不同復發間隔時間彼此的相關性,本文嘗試引入兩個脆弱變數分別解釋資料中兩種不同的關聯性,並以Wang 和 Chang(1999)所提出的加權風險集合法以及Hoffman et al.(2001)提出的群集內重抽法分別處理這些關聯性,針對群集復發資料結合兩法成為四種無母數的統計方法,估計其邊際間隔時間的存活函數,最後藉由模擬呈現此四種方法與過去僅考慮部分相關性或不考慮任何相關性的方法比較其估計結果。 | zh_TW |
dc.description.abstract | Clustered recurrent event data are a common data structure in longitudinal follow-up studies, in which more than one event of the same type within an individual may be observed during follow-up. The main outcome of interest is the gap time between two successive recurrent events. For clustered recurrent event data, there are two potential sources of correlation, within-individual correlation between recurrent gap times and within-cluster correlation between individual. The aim of this study is to estimate the marginal survival function of recurrent gap time by assuming that two latent frailty variables result in the within-individual and within-cluster correlations and recurrent gap times for an individual in the same cluster are identically independently distributed, We propose four non-parametric methods to estimate the marginal survival function of gap time by combining the weighted risk set method for recurrent gap time data (Wang and Chang, 1999) and within cluster resampling method for clustered survival data (Hoffman et al., 2001), to deal with the within subject correlation and within cluster correlation. A series of simulation studies are conducted to investigate the statistical properties and performance of the proposed methods and to compare with naive methods which ignore within-subject correlation, within-cluster correlation, or both. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T13:22:43Z (GMT). No. of bitstreams: 1 ntu-102-R00849032-1.pdf: 433187 bytes, checksum: a401f673e3248f70f7d0b6675aeafabd (MD5) Previous issue date: 2013 | en |
dc.description.tableofcontents | 致謝..........................................................................................................................................i
中文摘要..................................................................................................................................ii 英文摘要.................................................................................................................................iii 第一章導論 1 第一節前言 1 第二節研究動機 2 第二章文獻回顧 3 第一節 加權風險集合法 3 第二節群集內重複抽樣法與調整後群集內重複抽樣法 5 第三章方法 6 第一節 群集與復發事件分別使用加權風險集合法的組合 6 第二節 群集與復發事件分別使用群集內重抽法與調整後群集內重抽法的組合 7 第三節 群集與復發事件分別使用群集內重抽法與加權風險集合法的組合 8 第四節 群集與復發事件分別使用加權風險集合法與調整後群集內重抽法的組合 9 第四章模擬研究 11 第一節 模擬資料生成 11 第二節 模擬參數的設定 12 第三節 比較各方法的估計結果 14 第四節 對照方法介紹 17 第六節 模擬結果 18 第五章 結果與討論 66 參考文獻 68 | |
dc.language.iso | zh-TW | |
dc.title | 群集資料之復發間隔時間存活函數無母數估計 | zh_TW |
dc.title | Nonparametric Estimation of Recurrent Gap Time Survival Function for Clustered Data | en |
dc.type | Thesis | |
dc.date.schoolyear | 101-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 簡國龍(Kuo-Liong Chien),陳秀熙(Hsiu-Hsi Chen),鄭宗記(Tsung-Chi Cheng) | |
dc.subject.keyword | 間隔時間,群集復發事件,加權風險集合法,群集內重抽法,具訊息的群集大小, | zh_TW |
dc.subject.keyword | Clustered recurrent events,Gap times,Informative cluster size,Weighted risk set,Within cluster resampling, | en |
dc.relation.page | 68 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2013-07-25 | |
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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