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標題: | 貝氏網絡統合分析模型之比較 A Comparison of Bayesian Models for Network Meta-analysis |
作者: | Zong-Yue You 游宗軏 |
指導教授: | 杜裕康(Yu-Kang Tu) |
關鍵字: | 網絡統合分析,直接比較,間接比較,基礎參數,隨機效應,貝氏階層模型, pairwise meta-analysis,network meta-analysis,direct evidence,indirect evidence,basic parameters,random effects,Bayesian hierarchical model, |
出版年 : | 2014 |
學位: | 碩士 |
摘要: | 背景
隨著實證醫學的發展,有效率的統合所蒐集到的證據日趨重要且在醫療決策上的影響力也愈來愈大,統合分析因而成為系統性文獻回顧後不可或缺的統合證據之方法。傳統的統合分析僅針對兩個處置方式進行比較,若想以此比較多個處置方式,則必須進行多次的統合分析,本論文主要探討的網絡統合分析為在一個框架下同時比較多個處置方式的統計方法。而現今最主要的網絡統合分析之模型是由Lu和Ades兩位學者所提出的,於本論文中稱「Lu & Ades model」,該模型為一運行在統計軟體BUGS中的貝氏階層模型。 目標 由於Lu & Ades model在隨機效應的設定上有一定的複雜性,於本論文中我們嘗試開發在設定上更單純且調整彈性更大的網絡統合分析模型,並期望以此降低進入網絡統合分析領域之門檻。 方法 我們提出了一個新的模型並令名為「Random treatment effects model」,相較於傳統的方法,該模型有著設定容易且更具調整彈性等優點。並透過分析一真實的資料將其與其他網絡統合分析方法Lu & Ades model、Contrast model進行比較,其中Contrast model由Piepho等人於2012年所提出。而資料內容為不同戒菸方式之比較,其源自於1996年由Fiore等人在衛生保健政策和研究機構AHCPR的Smoking Cessation Guideline Panel報告。 結果 本論文使用上述的三個模型來分析戒菸資料,而在Random treatment effects model中,我們使用了兩種不同的隨機效應之設定方式,故從戒菸資料中可得四個分析結果。而四種模型得到非常接近的分析結果,均顯示戒菸效果由高至低的戒菸方式依序為Group counseling、Individual counseling、Self-help、No contact。 結論 Random treatment effects model在實際分析的效果上與目前主要的方法並沒有差別,而且可以使統計分析人員更直觀的理解模型,不僅如此,Random treatment effects model還可進一步地放寬Lu & Ades model隨機效應之變異必須相同的假設,使得模型的假設可以更接近真實的情況。 Background Since the emergence of evidence-based medicine movement, effective evidence synthesis becomes important for decision making for clinical researchers and policy makers. Meta-analysis of results from clinical trials is therefore an indispensable research tool for research synthesis. While traditional meta-analysis compares two treatment groups, network meta-analysis can compare more than two treatments within one statistical framework. The current Bayesian hierarchical model for network meta-analysis was first proposed by Lu and Ades with the use of the flexible statistical software WInBUGS. Objectives Because it is quite complex to set up Lu & Ades’s model, this research attempts to develop a new model which is simpler and more flexible. Consequently, the learning curve for clinical researchers to undertake network meta-analysis is less steep. Methods We propose a “Random Treatment Effects Model” and compare it to the Lu & Ades’s model and ”Contrast model” proposed by Piepho. We use a real data, which was from the AHCPR’s Smoking Cessation Guideline Panel by Fiore et al.,to illustrate the three models yields similar results, but our“Random Treatment Effects Model” is more intuitive and flexible. Results Two different random effect structures can be set up in Random Treatment Effects Model.There are no substantial differences in results between the four models, and the treatment effects in smoking cessation from high to low are group counseling、individual counseling、self-help and no contact. Conclusions The Random treatment effects model we proposed yields the same results as those from the Lu & Ades model. Furthermore, the model is more intuitive to understanding, and it has more flexibility to set up complex random effects structure, which is closer to the reality. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/57918 |
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顯示於系所單位: | 流行病學與預防醫學研究所 |
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