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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 | Hua Li | en |
dc.contributor.author | 李驊 | zh_TW |
dc.date.accessioned | 2021-07-11T15:41:05Z | - |
dc.date.available | 2021-10-11 | |
dc.date.copyright | 2018-10-11 | |
dc.date.issued | 2018 | |
dc.date.submitted | 2018-08-13 | |
dc.identifier.citation | 參考文獻
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/79065 | - |
dc.description.abstract | 研究目的
研究偏差的治療效果,在不同網絡結構與不同的模型中會如何影響估計值的結果。 研究方法 以頻率學派的架構執行Lu & Ades model、Baseline model和Arm-based model。Arm-based model的相關係數矩陣設定為治療彼此之間互相獨立,每個治療允許有各自的隨機效應。三種模型皆以二階段分析處理。第一階段先以固定效應模型估計,以其結果估計隨機效應變異數的值。第二階段將前面估計出的變異數作為隨機效應模型的權重參數,再估計(相對或絕對)治療效果。Arm-based model的絕對治療效果將被轉換成相對治療效果後,與其他兩個模型做比較。透過模擬不同的網絡結構下七種偏差情境,來探討偏差在不同模型下傳遞的模式。 研究結果 Lu & Ades model執行前需將治療做排序,一組成對比較中不論偏差的治療是哪一個,偏差的影響會往兩個治療中排序靠後的治療的估計值傳遞。當兩個治療偏差的方向與大小相同時,偏差影響會互相抵消使估計結果不受影響。偏差在Baseline model裡主要影響偏差的治療的相對效果估計值,因此偏差的影響較能如實呈現在對應治療的估計值上。但部分偏差的影響會透過基底治療傳遞到整個網絡,這部分對治療效果評估的影響類似Lu & Ades model,傾向傳遞給排序靠後的治療的估計值。Arm-based model的偏差會如實地在偏差的治療的相對效果估計值上觀察到,其他完全不會受到影響。 結論 三種模型的偏差情況各有特色,區別在於要由哪些估計值去吸收偏差的效果,因此不存在最好的模型。研究者可以根據資料有疑慮、可能存在偏差的部分,評估使用何種模型最有利,或是較能預期估計值偏差的大小與方向。 | zh_TW |
dc.description.abstract | Aims: To investigate how bias contained in direct evidence of one treatment contrast within a network meta-analysis will impact on the estimation of other treatment contrasts under the Lu & Ades model, baseline model, and arm-based model.
Methods: Simulations of the three statistical models for network meta-analysis within the frequentist framework were undertaken to evaluate the impact of bias in evidence on one treatment contrast. The within-study correlation structure in the arm-based model was assumed to be independent and heterogeneous. The analyses of the three models used two-step methods by first pooling together all pairwise comparisons for each treatment contrast and then undertaking a fixed effect network meta-analysis. This result was used to calculate a variance by the sum square of Pearson residual, and then in the second step we estimated the relative or absolute treatment effect with the variance becoming weighted parameter in random effect model. We simulated seven scenarios to evaluate how different geometrical structures of a network affect the impact of the bias in the evidence of a treatment contrast on the estimates of other treatment contrasts. Results: In Lu & Ades model, treatments were arranged in an order that started with the global baseline treatment. The bias in one treatment affected the estimate of treatments that were farther in the order. When the biases in the two treatments of a contrast had the same direction and magnitude, the estimated bias in this treatment contrast was cancelled out. In the baseline model, the bias in one treatments partially affected the estimate of this treatment, while some of the bias was propagated through the baseline treatment, yielding small bias in the other relative effects. Under the arm-based model, the effect of the biases did not spread to other treatment contrasts unrelated to the biased treatment. Conclusions: Bias propagation in three different models has various patterns which are also affected by the geometry of the network. When evidence of some treatments may contain biases, researchers could then evaluate which estimates may be affected by these biases and how much the potential impact is. | en |
dc.description.provenance | Made available in DSpace on 2021-07-11T15:41:05Z (GMT). No. of bitstreams: 1 ntu-107-R05849012-1.pdf: 4740898 bytes, checksum: 03a793960fe330d3d363ada8fb0c51bd (MD5) Previous issue date: 2018 | en |
dc.description.tableofcontents | 口試委員會審定書 i
中文摘要 ii Abstract iv 目錄 vi 表格目錄 viii 圖片目錄 ix 第一章 介紹 1 1.1研究背景 1 1.2研究目的 2 第二章 文獻回顧 3 2.1基本假設 3 2.1.1相似性(similarity) 3 2.1.2不一致性(inconsistency) 4 2.2兩種隨機效應假設 5 2.3試驗的偏差 5 第三章 研究方法 8 3.1 名詞定義 8 3.2 Lu & Ades model 10 3.2.1兩階段分析 10 3.2.2相關係數矩陣 13 3.2.3帽子矩陣(hat matrix)與貢獻圖(contribution plot) 14 3.3 Baseline model 16 3.3.1分離式模型(separate model)與補值 17 3.4 Arm-based model 18 3.4.1兩階段分析 18 3.5 模型比較 19 第四章 模擬 23 4.1模擬情境 23 4.2模擬結果 25 4.2.1 Lu & Ades model 31 4.2.2 Baseline model 32 4.2.3 Arm-based model 35 第五章 討論與結論 36 5.1 Lu & Ades model傳遞模式 36 5.2 Baseline model傳遞模式 39 5.3 Arm-based model傳遞模式 41 5.4研究限制 42 5.4.1建構在頻率學派下 42 5.4.2實際偏差狀況 44 5.5實際應用 44 5.6結論 45 參考文獻 47 附錄 49 R code 49 A. Lu & Ades model和貢獻圖 49 B. Baseline model 56 C. Arm-based model 61 | |
dc.language.iso | zh-TW | |
dc.title | 網絡統合分析中偏差的傳導模式 | zh_TW |
dc.title | Bias propagation in network meta-analysis models | en |
dc.type | Thesis | |
dc.date.schoolyear | 106-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 蕭朱杏(Chu-hsing Kate Hsiao),陳錦華(Jin-Hua Chen) | |
dc.subject.keyword | 網絡統合分析,contrast-based model,arm-based model,二階段模式,偏差, | zh_TW |
dc.subject.keyword | network meta-analysis,contrast-based model,arm-based model,two-stage model,bias, | en |
dc.relation.page | 62 | |
dc.identifier.doi | 10.6342/NTU201802673 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2018-08-13 | |
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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