English  |  正體中文  |  简体中文  |  Items with full text/Total items : 51491/86611 (59%)
Visitors : 8253170      Online Users : 77
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/103096


    Title: 應用ROC曲線下面積之統合分析與統合迴歸來探討潛在種族效應對青光眼診斷準確性之影響
    Other Titles: Application of Meta-Analysis and Meta-Regression in Area under ROC Curve to Explore the Potential Ethnicity Effect in the Accuracy of Glaucoma Diagnosis
    Authors: 張玉坤
    Contributors: 淡江大學數學學系
    Keywords: 統合分析;統合迴歸;ROC 曲線;ROC 曲線圖形下面積;光電同調斷層掃描儀;青光眼診斷;視神經纖維層;Meta analysis;Meta Regression;ROC Curve;Area under ROC Cure;Optical Coherence Tomography;Glaucoma Diagnosis;Retinal Nerve Fiber Layer
    Date: 2012-08
    Issue Date: 2015-05-18 16:18:20 (UTC+8)
    Abstract: 統合分析是將一些議題相關但彼此獨立的臨床實驗之研究結果(大都取材於已發表 之期刊論文),以量性加權平均的方法結合統整,用來代表此議題現階段之研究結果。 據此評估療效或草擬新的臨床實驗之依據。常用之統合分析方法有固定及隨機效應兩 種,且進一步依資料特性來分,有: 二元資料之相對危險度(Relative Risk)、勝算比(Odds Ratio)及率差(Rates Difference); 常態分布資料之效應量(Effect Size) 及統合迴歸(Meta Regression)等。至於臨床醫學診斷準確性常用之指標,ROC 曲線圖形下面積,的統合分 析方法,至今尚未被提出。 青光眼是不可逆的視神經病變,其疾病的特色就是漸進性的視神經纖維層(Retinal Nerve Fiber Layer,RNFL)厚度的變薄,其嚴重度可以客觀的由影像檢查儀進行評估,目 前眼科是以光電同調斷層掃描儀(Optical Coherence Tomography,OCT)為主。然而,受 測者之種族與年齡為臨床上認定之重要影響RNFL測量值之因素。現今發表之文獻以 OCT診斷青光眼之診斷力呈現顯著之異質性,所用診斷力指標常以靈敏度(Sensitivity)、 特異性(Specificity)及ROC曲線圖形下面積呈現。據此,以隨機效應之統合分析方法,綜 合ROC曲線圖形下面積,將有助於呈現以OCT診斷青光眼之整體診斷力。 本研究計畫將提出ROC曲線圖形下面積之固定及隨機效應的統合分析方法,並將之 應用在以OCT診斷青光眼的研究議題。藉由固定效應的統合分析方法中的異質性檢定 (Test for Heterogeneity),可驗證現今發表文獻以OCT診斷青光眼之診斷力是否存在異質 性。若確實存在,我們將進一步以統合迴歸探討受測者之種族、年齡與疾病嚴重度等因 素是否是影響OCT診斷青光眼之診斷力的因素。
    Meta analysis is a quantitative weighted average method to combine the results of related but independent studies (usually drawn from the published literatures) and synthesize summaries and conclusions which may be used to evaluate the therapeutic effects and/or plain new related clinical trial accordingly. The commonly used meta-analyses were fixed effects and random effects methods. According to the characteristic of data, meta-analyses can be further classified into: the relative risk, odds ratio, and rates difference for binary data and effect size and meta-regression for normally distributed data. Meta analysis for area under ROC curve (AUC), a commonly used guideline for the accuracy of medical diagnosis method, has not been proposed yet. Glaucoma is an irreversible optic neuropathy, which characterized by progressive retinal nerve fiber layer (RNFL) thinning. Its severity could be evaluated objectively by imaging techniques which mainly by optical coherence tomography (OCT) in current ophthalmology. However, subject’s ethnicity and age are considered as clinically important factors for RNFL measurements. The diagnostic capacities of OCT for glaucoma were heterogeneous in the current published literatures. Most of them were presented in terms of sensitivity, specificity, and area under ROC curve (AUC). Accordingly, a random effects’ meta-analysis for AUC will be helpful to synthesize the overall diagnostic capacities of OCT for glaucoma. In this study, we are going to propose a fixed/random effects meta-analysis method for area under ROC curve and apply it to glaucoma diagnostic by OCT. The application of testing homogeneity in the fixed effect meta-analysis can be used to verify that whether there exists heterogeneity in the accuracy among those published papers in glaucoma diagnosis using OCT. We will further analyze the effects of potential risk factors, ethnicity, age and severity, to the diagnostic capacities of OCT for glaucoma.
    Appears in Collections:[數學學系暨研究所] 研究報告

    Files in This Item:

    There are no files associated with this item.

    All items in 機構典藏 are protected by copyright, with all rights reserved.


    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback