博碩士論文 105225014 詳細資訊




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姓名 張雯婷(Wen-Ting Chang)  查詢紙本館藏   畢業系所 統計研究所
論文名稱 加速失效模型與Cox風險迴歸模型之模型選擇以時間相依AUC及預測精準度為指標
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摘要(中) 傳統上接受者作業特徵曲線(ROC)是針對二元的分類結果進行預測,然而存活資料為結合二元設限狀態及連續存活時間的資料型態,因此若將敏感度與特異度的定義經過適當的修改後,即可將接受者作業特徵曲線應用於存活資料上。在過去文獻中,已有學者將其推廣到時間獨立共變數下配適Cox比例風險模型,並結合時間相依敏感度與特異度預測精準度。然而在部分的醫學研究中,常有資料不符合比例風險假設,因此我們建議以參數化加速失效模型取代Cox比例風險模型,結合時間相依敏感度與特異度,並以接受者作業特徵曲線下面積(AUC)及一致性指標Concordance判斷生物指標對疾病的區別能力,亦進一步擴展此方法到含有長期追蹤共變數的資料上。
摘要(英) Traditionally, the receiver operating characteristic curve (ROC) are used to predict the binary classification results. However, survival data are data types that combine the binary censored status and continuous survival time. Therefore, if the definition of sensitivity and specificity have been slightly modified, the ROC curve can be applied to the survival data. In the past literature, some scholars have extended it when conditioned at time-independent covariate to fit Cox proportional hazard model, and combined with time-dependent sensitivity and specificity to predict model accuracy. However, in some medical studies, there are often data that violate the proportional hazard assumption. Therefore, we recommend to replace the Cox proportional hazard model as the parametric accelerated failure time model with combining time-dependent sensitivity, specificity, and AUC. And finally use AUC and Concordance to evaluate the ability of biomarkers to discriminate diseases and further extended this method to longitudinal covariates.
關鍵字(中) ★ 接受者作業特徵曲線下面積
★ 時間相依接受者作業特徵曲線下面積
★ 事件型敏感度
★ 動態型特異度
★ 預測
★ 一致性指標
★ Cox風險迴歸模型
★ 加速失效模型
關鍵字(英)
論文目次 第一章 序論 1
1.1 傳統的ROC曲線分析……………………………………….2
1.1.1 敏感度與特異度………………………………………..2
1.1.2 ROC曲線……………………………………………….3
1.1.3 建構ROC……………………………………………….4
1.1.4 建構AUC……………………………………………….6
1.2 推廣的ROC曲線分析………………………………………..9
1.2.1 時間相依的ROC曲線…………………………………9
1.2.2 時間相依的AUC和一致性(Concordance)…………..11
1.3 Cox比例風險與長期追蹤資料之聯合建模………………..16
1.4 參數化AFT模型與長期追蹤資料之聯合建模……………17
第二章 統計方法 18
2.1 Cox迴歸模型………………………………………………..19
2.2 AFT迴歸模型……………………………………………….22
2.2.1 Weibull迴歸模型……………………………………..24
2.2.2 Loglogistic迴歸模型…………………………………25
2.2.3 Lognormal迴歸模型…………………………………26
第三章 模擬研究 28
3.1 時間固定共變數…………………………………………….28
3.1.1 資料生成來自Weibull Cox模型…………..................28
3.1.2 資料生成來自 Weibull AFT模型……………………32
3.1.3 資料生成來自 Loglogistic Cox模型……...................36
3.1.4 資料生成來自 Loglogistic AFT模型……..................40
3.1.5 資料生成來自 Lognormal Cox模型….......................44
3.1.6 資料生成來自 Lognormal AFT模型……..................48
第四章 資料分析 52
4.1 資料背景與分析…………………………………………..52
4.2 分析結果…………………………………………………..54
第五章 總結與討論 65
參考文獻 67
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中華民國衛生福利部疾病管制署(2018)。傳染病防治工作手冊。
張雅玟(2015)。三種時間相依的接受者作業特徵曲線下面積估計方法之
比較與修正。國立中央大學統計研究所碩士論文。
林園馨(2016)。Model-base Time-dependent AUC and Predictive Accuracy.
國立中央大學統計研究所碩士論文。
指導教授 曾議寬 審核日期 2018-7-25
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