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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 | 杜裕康(Yu-Kang Tu) | |
dc.contributor.author | Yu-Chen Kuo | en |
dc.contributor.author | 郭羽晨 | zh_TW |
dc.date.accessioned | 2021-06-16T07:06:32Z | - |
dc.date.available | 2016-10-20 | |
dc.date.copyright | 2014-10-20 | |
dc.date.issued | 2014 | |
dc.date.submitted | 2014-07-10 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/57836 | - |
dc.description.abstract | 背景:
傳統統合分析只能處理兩種治療方式之間的比較,為了比較超過兩種治療方式而發展出網絡統合分析。不一致性在網絡統合分析中是一個極為重要的議題。不一致性的定義為直接證據與間接證據之間的差異,但在最新研究將不一致性分為傳統不一致性與design不一致性。Design不一致性的出現使網絡統合分析中不一致性的問題更加複雜。 目的: 本論文的研究目的在於比較文獻當中各式估計不一致性的模型,以及各種檢驗不一致性的方法,並對不同不一致性的定義做深入的探討。 方法: 我們比較了五種不同評估不一致性的方法,並使用戒菸資料與血栓溶解藥劑資料作為分析案例。首先是Lu & Ades模型,此模型將組間差異參數分為基礎參數與功能參數,並首次對不一致性做定義,對之後的網絡統合分析發展有很大的影響。其二是Unrelated Mean Effect模型,此模型估計了所有的組間差異參數,其結果可視為個別做傳統統合分析的結果。其三為Back-Calculation檢驗方法,此方法可檢驗網絡中哪兩組比較有不一致性。其四為Higgins & White模型,首次納入design概念的模型,並估計design不一致性參數。最後為Krahn模型,融合傳統統合分析與最新的design概念,並使用淨熱圖來顯示網絡中的design不一致性。 結果: 我們從不同層面來探討五種方法的優缺點:Lu & Ades模型雖然無法處理design不一致性,但此模型相較於其他模型最為穩健。Unrelated Mean Effect模型雖然無需一致性假設與不一致性參數,但其缺少了整體網絡的觀點。Back-Calculation檢驗方法能檢驗出網絡中不一致性的確切位置,但無法檢驗design不一致性。Higgins & White模型是第一個能處理design不一致性的模型,但其design不一致性參數不易設定及解釋。Krahn模型中的淨熱圖能顯示design不一致性的確切位置,但模型本身的估計結果無考慮異質性與不一致性。 結論: 我們以客觀的角度認為Lu & Ades模型是最佳的選擇,相較於其他模型,其結果直觀、易解釋,模型建構也很好理解,並可使用Back-Calculation檢驗方法更進一步檢驗不一致性。 | zh_TW |
dc.description.abstract | Background:
Pairwise meta-analysis can only deal with comparisons between two treatments. Network meta-analysis is a new research synthesis method for comparing more than two different treatments. Inconsistency is an important issue in network meta-analysis and is defined as the difference between direct evidence and indirect evidence. There are two types of inconsistency in the literature, namely, “the loop inconsistency” and “the design inconsistency”. Objective: The main objective of this study is to compare different inconsistency models and different methods for evaluating inconsistency from both theoretical and practical perspectives with use of real examples for illustration. Methods: Five methods are compared for assessing inconsistency. Lu & Ades model compare estimating for differences in treatment effects between the uses of basic parameters and functional parameters; it is the first model that defines inconsistency. Unrelated Mean Effect model estimate all differences between pairs of treatments based on direct evidence. Unrelated Mean Effect model’s results can be viewed as results of multiple pairwise meta-analyses. Back-Calculation method evaluates the inconsistency in the whole network between direct and indirect evidence. Higgins & White model defines the inconsistency as the differences between study designs, i.e. the treatments compared in each study. The differences within each study design is considered heterogeneity Last, Krahn model combines pairwise meta-analysis with design inconsistency; it then uses the net heat plot to demonstrate where the design inconsistency is in network. Results: Pros and cons of the five methods and the potential problems arising from the analysis are discussed. Lu & Ades model is more robust than other models, but it cannot estimate design inconsistency. Unrelated Mean Effect model need neither the consistency assumption nor inconsistency parameters, but the model does not provide a holistic evaluation for the whole network. Back-Calculation method checks inconsistency in network but not design inconsistency. Higgins & White model is the first model to estimate design inconsistency parameters, but there were difficulties in setting and explaining the design inconsistency parameters. The net heat plot evaluates design inconsistency, but the Krahn model estimates differences between treatments without taking heterogeneity and inconsistency into account. Conclusions: Lu & Ades model is considered the best model for evaluating inconsistency in network meta-analysis than other models. Results from Lu & Ades model are easy to understand and interpret. Back-Calculation method can be conducted along with Lu & Ades’s model for identifying inconsistency in network meta-analysis. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T07:06:32Z (GMT). No. of bitstreams: 1 ntu-103-R01849033-1.pdf: 1942954 bytes, checksum: 53de2a740be2007598d5b3f891ae387a (MD5) Previous issue date: 2014 | en |
dc.description.tableofcontents | 致謝 i
摘要 ii Abstract iv 目錄 vi 表目錄 viii 圖目錄 ix 第一章 前言 1 一、 研究背景 1 二、 研究目的 3 第二章 文獻回顧 4 一、 傳統不一致性回顧 4 二、 Design不一致性回顧 8 第三章 研究材料與方法 12 一、 資料來源 13 1. 戒菸資料 13 2. 血栓溶解藥劑資料 16 二、 模型與檢驗方法 21 1. Lu & Ades 模型 21 2. Unrelated Mean Effect模型 24 3. Back-Calculation檢驗方法 26 4. Higgins & White模型 28 5. Krahn模型 36 第四章 分析結果 40 一、 Lu & Ades 模型 41 二、 Unrelated Mean Effect模型 46 三、 Back-Calculation檢驗方法 50 四、 Higgins & White模型 53 五、 Krahn模型 56 第五章 討論與結論 60 一、 討論 60 1. 模型建立層面 60 2. 檢驗方法層面 63 3. 軟體操作層面 65 4. 結果應用層面 67 二、 結論 70 參考文獻 72 附錄 74 程式 74 1. Lu & Ades模型 WinBUGs code 74 2. Unrelated Mean Effect模型 77 3. Higgins & White模型 78 | |
dc.language.iso | zh-TW | |
dc.title | 網絡統合分析中多種不一致性模型之比較 | zh_TW |
dc.title | The comparison of various inconsistency models in network meta-analysis | en |
dc.type | Thesis | |
dc.date.schoolyear | 102-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 李文宗(Wen-Chung Lee),張啟仁(Chee-Jen Chang) | |
dc.subject.keyword | 網絡統合分析,直接比較,間接比較,傳統不一致性,design不一致性,基礎參數, | zh_TW |
dc.subject.keyword | pairwise meta-analysis,network meta-analysis,direct evidence,indirect evidence,loop inconsistency,design inconsistency,basic parameters, | en |
dc.relation.page | 83 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2014-07-10 | |
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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