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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.advisor | 張淑惠(Shu-Hui Chang | shuhui@ntu.edu.tw | ), | |
dc.contributor.author | Zhen-Yu Lian | en |
dc.contributor.author | 連振宇 | zh_TW |
dc.date.accessioned | 2023-03-20T00:07:49Z | - |
dc.date.copyright | 2022-10-20 | |
dc.date.issued | 2021 | |
dc.date.submitted | 2022-08-04 | |
dc.identifier.citation | Anderson, J. E., Louis, T. A., Holm, N. V. & Harvald, B. (1992). Time-dependent association measures for bivariate survival distributions. Journal of the American Statistical Association 87, 641-650. Chang, S. H. & Wang, M. C. (1999) Conditional Regression Analysis for Recurrence Time Data, Journal of the American Statistical Association, 94:448, 1221-1230, DOI:10.1080/01621459.1999.10473875 Clayton, D. G. (1978) A model for association in bivariate life tables and its application in epidemiological studies of familial tendency in chronic disease incidence. Biometrika 65, 141–51. Fleming, T. R. & Harrington D. P. (1991). Counting Process and Survival Analysis. Wiley: New York. Fu, T. C., Su, D. H., Chang, S. H. (2016) Serial association analyses of recurrent gap time data via Kendall's tau. Biostatistics 17, 188–202. Hu, T., B. Nan & X. Lin. (2019) Proportional cross-ratio model. Lifetime Data Analysis 25, 480–506. Hu, T., B. Nan, X. Lin & J. M. Robins. (2011) Time-dependent cross ratio estimation for bivariate failure times. Biometrika 98, 341–54. Lakhal-Chaieb, L., Cook, R. J. & Lin, X. (2010). Inverse probability of censoring weighted estimated of Kendall’s tau for gap time analyses. Biometrics 66, 1145–1152. Lin, D. Y. Wei, L. J. Yang, I. & Ying, Z. (2000) Semiparametric Regression for the Mean and Rate Functions of Recurrent Events. Journal of the Royal Statistical Society. Series B (Statistical Methodology), 2000,Vol. 62, No. 4 (2000), 711-730. Nan, B., X. Lin, L. D. Lisabeth & S. D. Harlow. (2006) Piecewise constant cross-ratio estimation for association of age at a marker event and age at menopause. Journal of the American Statistical Association 101, 65–77. 張淑惠 (2017) 重複交替雙間隔時間之相依結構分析(第2 年)。中華民國科技部專題研究計畫。MOST 105-2118-M-002-001-MY2。 喻承俊 (2016) 復發有序二元間隔時間資料的事件別相關性分析。國立台灣大學共同教育中心統計碩士學位學程碩士論文。 施雅芝 (2017) 具時間區塊交叉比半參數估計。國立台灣大學公共衛生學院流行病學與預防醫學研究所生物醫學統計組碩士論文 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/86633 | - |
dc.description.abstract | 在長期追蹤研究中,個體自起始事件至研究結束前可能會經歷二元有序事件。舉例而言,慢性病患之出院與再住院即是二元有序事件。以常數和隨時間改變之交叉比分析二元有序事件中的第一與第二間隔時間之相依性蘊含慢性病人疾病歷程資訊。此相依性可能會受病患特質,例如性別與基因效應或其他有關之因子等影響。因此,我們針對二元有序間隔時間之交叉比提出參數化之對數線性模型。此參數化之交叉比模型可以用針對第二間隔時間給定共變數與第一間隔時間條件風險之分層比例風險模型其風險比來表示,且此比例風險模型不須額外假設第一與第二間隔時間之邊際分布形式。此具共變數相依交叉比之半參數估計可由最大化分層加權部份概似函數方法來得到,其中以倒數機率設限權重來處理第二間隔時間之誘導相依設限。我們同時證明此具共變數相依交叉比估計具有一致性與漸進常態分布性質,並以模擬分析檢驗所提出估計式之有限樣本表現量。提出之方法也應用於結腸癌資料,以自進入研究至發生結腸癌復發與發生結腸癌復發至死亡之二元有序間隔時間,估計其與共變數相關之相依交叉比。 | zh_TW |
dc.description.abstract | In longitudinal follow-up studies, individual may experience ordered bivariate events before the end of the study. For instance, discharge and rehospitalization are ordered bivariate events for patients having a chronic disease. Association analysis between the first and second gap times between ordered bivariate events in terms of timeinvariant and time-varying cross ratios may provide predictive information on the course of chronic disease. Such association may be affected by patient’s characteristics, such as gender, genome type and other related factors. Therefore, we introduce parametric loglinear models for cross ratio between ordered bivariate gap times. These parametric cross ratio models can be expressed in terms of hazard ratios from stratified proportional hazards models via conditional hazards for the second gap times given the first gap time and covariates without specifying the marginal distributions of the first and second gap times. Then, semiparametric estimates of the covariate-dependent cross ratios can be obtained by maximizing the stratified weighted partial likelihood in which the inverse probability of censoring weights is used to tackle the induced dependent censoring of the second gap time. We have shown the consistency and asymptotic normality of the estimated covariate-dependent cross ratios. The finite-sample performance of the proposed methods is examined by simulation studies. The proposed method is also applied to the colon data for the estimation of the covariate-dependent cross ratios for the first gap time from entry study to recurrent colon cancer and the second gap time from recurrent colon cancer to death. | en |
dc.description.provenance | Made available in DSpace on 2023-03-20T00:07:49Z (GMT). No. of bitstreams: 1 U0001-0408202210370300.pdf: 1772773 bytes, checksum: e6bd70382ae005bdaa6ffa3e2e14bc3a (MD5) Previous issue date: 2021 | en |
dc.description.tableofcontents | 誌謝 ................................................................. i 中文摘要 ............................................................ ii 英文摘要 ........................................................... iii 第一章 序論 .......................................................... 1 1.1 前言 ...................................................................................................................... 1 1.2 研究動機與目的 .................................................................................................. 1 第二章 文獻回顧 ............................................................................................................. 3 2.1 二元事件時間資料之相依性測量 ...................................................................... 3 2.1.1 交叉比 ........................................................................................................ 4 2.1.2 分段常數交叉比 ........................................................................................ 7 2.1.3 具共變數之條件交叉比 ............................................................................ 7 2.2 二元事件時間資料之交叉比估計 ...................................................................... 8 2.2.1 時間相依交叉比估計方式 ........................................................................ 8 2.2.2 常數交叉比估計方式 ................................................................................ 9 2.2.3 分段常數交叉比估計方式 ...................................................................... 10 2.3 以倒數機率設限權重 (IPCW) 調整誘導訊息偏差 ....................................... 11 第三章 方法 ................................................................................................................... 13 3.1 符號定義與假設 ................................................................................................ 13 3.2 具共變數之條件交叉比定義與參數化對數線性交叉比模型 ........................ 14 3.2.1 具共變數二元有序間隔時間之常數條件交叉比模型 .......................... 14 3.2.2 具共變數二元有序間隔時間之分段常數條件交叉比模型 .................. 15 3.3 估計方法 ............................................................................................................ 17 3.3.1 具共變數二元有序間隔時間之常數條件交叉比估計 .......................... 17 3.3.2 具共變數二元有序間隔時間之分段常數條件交叉比估計 .................. 18 3.4 大樣本理論 ........................................................................................................ 18 第四章 模擬分析 ........................................................................................................... 21 4.1 具共變數二元有序間隔時間之常數條件交叉比資料生成 ............................ 21 4.1.1 克萊頓條件機率生成法 .......................................................................... 21 4.1.2 克萊頓脆弱性生成法 .............................................................................. 22 4.1.3 克萊頓分布模擬情境與結果 .................................................................. 22 4.2 具共變數二元有序間隔時間之分段常數條件交叉比資料生成 .................... 24 4.2.1 克萊頓分段分布模擬情境與結果 .......................................................... 25 4.3 具共變數二元有序間隔時間對數常態資料生成 ............................................ 26 4.3.1 二元對數常態分布模擬情境與結果 ...................................................... 27 第五章 實例資料分析 ................................................................................................... 29 第六章 結果與討論 ....................................................................................................... 37 參考文獻 ......................................................................................................................... 39 附錄 .................................................................................................................................... 附錄一 模擬結果表格 ............................................................................................. 42 附錄二 交叉比二階偏微分方程之解為克萊頓相依結構 ..................................... 68 附錄三 特定連續時間點下交叉比為勝算比 ......................................................... 69 附錄四 克萊頓相依結構等價伽瑪脆弱性模型 ..................................................... 70 附錄五 倒數機率設限權重校正估計式 ................................................................. 71 附錄六 交叉比與重新參數化克萊頓相依結構關係 ............................................. 73 附錄七 克萊頓條件機率生成法 ............................................................................. 74 附錄八 克萊頓脆弱性生成法 ................................................................................. 75 附錄九 克萊頓分段分布生成法 ............................................................................. 79 附錄十 估計式之一致性與大樣本理論證明與漸進變異數估計 ......................... 80 | |
dc.language.iso | zh-TW | |
dc.title | 二元有序間隔時間具共變數相依交叉比半參數估計 | zh_TW |
dc.title | Semiparametric Analysis of Covariate-Dependent Cross Ratio for Ordered Bivariate Gap Times | en |
dc.type | Thesis | |
dc.date.schoolyear | 110-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 杜裕康(Yu-Kang Tu),蔡政安(CHEN-AN TSAI) | |
dc.subject.keyword | 克萊頓相依結構,交叉比,間隔時間,誘導訊息設限,倒數機率設限權重,擬加權部份概似, | zh_TW |
dc.subject.keyword | Clayton dependence structure,Cross ratio,Gap times,Induced informative censoring,Inverse probability censoring weighted,Pseudo weighted partial likelihood, | en |
dc.relation.page | 88 | |
dc.identifier.doi | 10.6342/NTU202202042 | |
dc.rights.note | 同意授權(全球公開) | |
dc.date.accepted | 2022-08-05 | |
dc.contributor.author-college | 共同教育中心 | zh_TW |
dc.contributor.author-dept | 統計碩士學位學程 | zh_TW |
dc.date.embargo-lift | 2022-10-20 | - |
顯示於系所單位: | 統計碩士學位學程 |
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