ROC曲線下面積之統合迴歸分析法 : 青光眼診斷之應用

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題名:	ROC曲線下面積之統合迴歸分析法 : 青光眼診斷之應用
其他題名:	Meta regression analysis method for area under ROC curve : Application to Glaucoma Diagnosis
作者:	何仲遠;Ho, Chung-Yuan
貢獻者:	淡江大學數學學系碩士班張玉坤
關鍵詞:	統合分析;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;ROC Curve;Area under ROC Cure(AUC);Optical Coherence Tomography(OCT);Glaucoma Diagnosis;Retinal Nerve Fiber Layer
日期:	2011
上傳時間:	2011-12-28 18:13:00 (UTC+8)
摘要:	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依資料特性有: 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。至於臨床醫學診斷常用之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，以統合迴歸之分析方法，探討ROC曲線圖形下面積異質性之可能影響因素，將有助於呈現以OCT診斷青光眼之整體診斷力。 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 study accordingly. The commonly used meta-analyses, dependent on the characteristic of data, are: 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 medical diagnosis method, has not been proposed yet. Glaucomais 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, meta regression analysiscan be used to explore the potential prognostic factors for the heterogeneityof AUC that will be helpful to synthesize the overall diagnostic capacities of OCT for glaucoma.
顯示於類別:	[數學學系暨研究所] 學位論文

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