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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 陳秀熙 | |
dc.contributor.author | Yi-Chun Lin | en |
dc.contributor.author | 林怡君 | zh_TW |
dc.date.accessioned | 2021-06-13T00:13:08Z | - |
dc.date.available | 2008-08-08 | |
dc.date.copyright | 2007-08-08 | |
dc.date.issued | 2007 | |
dc.date.submitted | 2007-07-27 | |
dc.identifier.citation | Amaya Cruz GP, B. G. (1996). 'Fuzzy gating and problem of screening.' Artificial Intelligence in Med 8: 377-385.
Ballantyne CM, H. A., Ferlic LL, et al. (1999). 'Influence of low HDL on progression of coronary artery disease and response to Fluvastatin therapy.' Circulation 99: 736-743 Bots ML, E. P., Nikitin Y, Salonen JT, Freire de Concalves A, Inzitari D, Sivenius J, Benetou V, Tuomilehto J, Koudstaal PJ, Grobbee DE. (2002). 'Total and HDL cholesterol and risk of stroke. EUROSTROKE: a collaborative study among research centres in Europe.' J Epidemiol Community Health 56 Suppl 1: i19-24. Bowman TS, S. H., Ma J, Kurth T, Kase CS, Stampfer MJ, Gaziano JM. (2003). 'Cholesterol and the risk of ischemic stroke.' Stroke. 34: 2930-4. Deverill M, Stephen Robson S (2006). 'Women's preferences in screening for Down syndrome.' Prenat Diagn 26: 837–841. Dubois D., P., H. (1998). 'An introduction to fuzzy systems.' Clin Chim Acta 270(1): 1-29. Furlong W, F. D., Torrance GW, Barr R, Horsman J. (1990). 'Guide to design and development of health-state utility instrumentation. Centre for Health Economics and Policy Analysis Working Paper #90–9. McMaster University: Hamilton, Ontario,Canada.' Kuppermann M, F. D., Gates E, Posner SF, Blumberg B, Washington AE. (1999). 'Preferences of women facing a prenatal diagnostic choice: long-term outcomes matter most.' Prenat Diagn 19: 711–716. Kuppermann M, S. S., Feeny D, Elkin EP, Washington AE. (1997). 'Can preference scores for discrete states be used to derive preference scores for an entire path of events? An application to prenatal diagnosis.' Med Decis Making 17(1): 42–55. Linden A (2006). 'Measuring diagnostic and predictive accuracy in disease management: an introduction to receiver operating characteristic (ROC) analysis.' J Eval Clin Pract 12(2): 132-9. Marie-Josée Dion, M., Pierre Tousignant, MD, MSc, Jean Bourbeau, MD, MSc, Dick Menzies, MD, MSc, Kevin Schwartzman, MD, MPH (2002). 'Measurement of Health Preferences among Patients with Tuberculous Infection and Disease.' Med Decis Making 22(Suppl): S102-S114 Milionis HJ, L. E., Goudevenos J, Bairaktari ET, Seferiadis K, Elisaf MS. (2005). ' Risk factors for first-ever acute ischemic non-embolic stroke in elderly individuals.' Int J Cardiol. 99(2): 269-75 Qizilbash N, J. L., Warlow C, Mann J. (1991). 'Fibrinogen and lipid concentrations as risk factors for transient ischaemic attacks and minor ischaemic strokes.' BMJ 303: 605-9. Sacco RL, B. R., Kargman DE, Boden-Albala B, Tuck C, Lin IF, Cheng JF, Paik MC, Shea S, Berglund L. (2001). ' High-density lipoprotein cholesterol and ischemic stroke in the elderly: the Northern Manhattan Stroke Study.' JAMA 285(21): 2729-35. Shahar E, C. L., Rosamond WD, Boland LL, Ballantyne CM, McGovern PG, Sharrett AR. (2003). 'Plasma lipid profile and incident ischemic stroke: the Atherosclerosis Risk in Communities (ARIC) study.' Stroke. 34: 623-31. Simona Giampaoli, L. P., Salvatore Panico, Diego Vanuzzo, Marco Ferrario, Paolo Chiodini, Lorenza Pilotto, Chiara Donfrancesco, Giancarlo Cesana, Roberto Sega, and Jeremiah Stamler (2006). 'Favorable Cardiovascular Risk Profile (Low Risk) and 10-Year Stroke Incidence in Women and Men: Findings from 12 Italian Population Samples.' Am J Epidemiol 163: 893–902. Steimann, F. (2001). 'On the use and usefulness of fuzzy sets in medical AI.' Artificial Intelligence in Med 21: 131-137. Thomas A. Pearson, M., PhD; Steven N. Blair, PED; Stephen R. Daniels, MD, PhD; Robert H. Eckel, MD; Joan M. Fair, RN, PhD; Stephen P. Fortmann, MD; Barry A. Franklin, PhD; Larry B. Goldstein, MD; Philip Greenland, MD; Scott M. Grundy, MD, PhD; Yuling Hong, MD, PhD; Nancy Houston Miller, RN; Ronald M. Lauer, MD; Ira S. Ockene, MD; Ralph L. Sacco, MD, MS; James F. Sallis, Jr, PhD; Sidney C. Smith, Jr, MD; Neil J. Stone, MD; Kathryn A. Taubert, PhD (2002). 'AHA Guidelines for Primary Prevention of Cardiovascular Disease and Stroke: 2002 Update Consensus Panel Guide to Comprehensive Risk Reduction for Adult Patients Without Coronary or Other Atherosclerotic Vascular Diseases.' Circulation 106: 388-391. Tirschwell DL, S. N., Heckbert SR, Lemaitre RN, Longstreth WT Jr, Psaty BM. (2004). 'Association of cholesterol with stroke risk varies in stroke subtypes and patient subgroups.' Neurology 63(10): 1868-75. Wald N, C. H., Royston P. (1988). 'Antenatal screening for Down syndrome.' Lancet 2(8624): 1362. Wannamethee SG, S. A., Ebrahim S. (2000). ' HDL-Cholesterol, total cholesterol, and the risk of stroke in middle-aged British men.' Stroke 31: 1882-8. Xu J, G. Y., Pan S, Liu F, Shi Y. (2006). 'A preoperative and intraoperative predictive model of prolonged intensive care unit stay for valvular surgery.' J Heart Valve Dis 15(2): 219-24. Yoshiyuki Soyama, D. K. M., MD, PhD; Yuko Morikawa, MD, PhD; Muneko Nishijo, MD, PhD; Yumiko Nakanishi, MD, PhD; Yuchi Naruse, MD, PhD; Sadanobu Kagamimori, MD, PhD; Hideaki Nakagawa, MD, PhD (2003). 'High-Density Lipoprotein Cholesterol and Risk of Stroke in Japanese Men and Women The Oyabe study.' Stroke 34: 863-868. Zadeh LA (1965). 'Fuzzy sets.' Inform Control 8: 338-353. Zahan, S. (1999). 'A fuzzy approach to computer-assisted myocardial ischemia diagnosis.' Artificial Intelligence in Med 17: 271-275. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/28586 | - |
dc.description.abstract | 前言
選擇一個適當的篩檢工具及篩檢標準,有助於早期偵測疾病的發生。目前,對於慢性疾病相關生物標記切點值的建立,多是建立在大樣本理論的架構下,對於個人主觀的選擇較少被討論。因此,本文主要利用一研究獲得錯誤分組之效用;接著建立臨床切點模式,與前述之效用結果結合,以印證在效用介入下切點值之改變並比較之。 材料及方法 本研究主要分成二部份,第一部份是利用標準博奕法及直接目測法測得比例尺度生物標記與預測腦中風發生之真陽性、真陰性、偽陽性、偽陰性四種情境之效用;第二部份是利用第一部份所得到四種情境之效用值配合貝氏最低成本決策法則及作業接受曲線方法,以總膽固醇及高密度膽固醇和腦中風發生為例,進行考慮個人喜好之效用下之切點值建立。 研究結果 在效用結果方面,69位受測者中,其中男性有30人,女性有39人,平均年齡為37.16±9.99歲。對於真陽性,真陰性,偽陽性和偽陰性的情境效用上,由高到低的排序為真陰性、偽陽性、真陽性、偽陰性。效用值在標準博奕法I為87.53, 81.17, 75.08, 63.06;標準博奕法II為 86.74, 83.64, 80.02, 64.68;在直接目測法為83.17, 74.32, 63.87, 44.16。男性,收入高者,也是有抽煙喝酒習慣者,是較為類似的一群。呈現出效用較高的結果。在效用矩陣R0/R1中,R0<R1,代表大家在希望確認自己是真正有病的效用是高於希望確認自己是真正沒病的效用。換句話說,大家比較在意他真正有病的狀態,至於沒病,是不是被誤判,在意的程度就比較低了。 以貝氏最低成本決策法則之切點決定結果,不做效用調整時,HDL最適切點為40.3mg/dL,膽固醇最適切點為252.4 mg/dL 以標準博奕法I所得之效用比調整,HDL最適切點為42.5 mg/dL,膽固醇最適切點為248.6 mg/dL 以標準博奕法II所得之效用比調整,HDL最適切點為46.0 mg/dL,膽固醇最適切點為242.8 mg/dL 以直接目視法所得之效用比調整,HDL最適切點為43.1 mg/dL,膽固醇最適切點為247.6 mg/dL 三個測量方法中,HDL皆較未調整效用比時為高,膽固醇皆較未調整效用比時為低。 結論 對於預測慢性疾病的生物標記切點值的建立,考量到偽陽性及偽陰性的效用值是有意義的。而貝氏最低成本效益法則可解決在效用值影響下的比例尺度生物標記對於預測慢性疾病發生的切點值之決定。 | zh_TW |
dc.description.abstract | Objectives
Population based screening for a chronic disease using an interval scale biomarker is often involved in selecting an optimal cutoff point. Selecting the optimal cut off point is faced with the misclassification between correct decision and alternative decision. The value of screening and selection of an optimal cutoff point depends on personal preference. High density lipoprotein (HDL) is one of protective factors for cerebral infarct. The cut off point of HDL related the outcome of cerebral infarct may vary from individual to individual. In this paper, we aimed to investigate the utility of misclassification by an illustration of the relationship of HDL to cerebral infarct. We also use the clinical model combined with above utility to prove the change of the cut off point of the interval scale biomarkers. Methods The study divided to two parts: the first part is that we obtain the utility scores of four scenarios of TP, TN, FP and FN with the relationship of HDL to cerebral infarct.by the standard gamble (SG) and visual analogue scale (VAS) approaches. The second part is that we use Bayes’ minimized cost decision rule and ROC curve method combined with utility scores of above four scenarios to determine the optimal cut-off point of HDL for cerebral infarct. Results Of the 69 people who completed the study, 30(43%) were men and 39(57%) were women, the mean age was 37.16±9.99 years old. The utility score of TN among four scenarios were ordered the highest followed by, FP, TP and FN. The utility scores in standard gamble I was 87.53, 81.17, 75.08, 63.06; in standard gamble II was 86.74, 83.64, 80.02, 64.68; in visual analogue scale was 83.17, 74.32, 63.87, 44.16. For personal characteristics, males who have higher income and have habits of smoking and drinking had higher utility of scenarios. The regret between TN and FP was smaller than that between TP and FN. The results of cut-off value for HDL and Cholesterol performed by Baye’s minimum cost decision rule were that in general population, the cut-off value for HDL and Cholesterol was defined as 40.3 and 252.4 without utility adjustment. The cut-off value for HDL and Cholesterol was defined as 42.5 and 248.6, given utility adjustment from standard gamble I at slope of 31.3. The cut-off value for HDL and Cholesterol was defined as 46.0 and 242.8, given utility adjustment from standard gamble II at slope of 11.9.The cut-off value for HDL and Cholesterol was defined as 43.1 and 247.6, given utility adjustment from visual analogue scale at slope of 26.5. That means utility ratio increases with the level of HDL at decreasing rate and decreases with the level of Cholesterol at decreasing rate. Conclusion The utility of TP, TN, FP and FN involved in population-based screening has been measured by using an example of HDL related to cerebral infarct. The considering the utility of FP and FN is meaningful for the selection of a cut off point of a biomarker related to a disease outcome. Besides, Bayes’ minimum cost decision rule was proposed to solve the problem of selecting optimal cutoff point for chronic diseases with interval scale variable. | en |
dc.description.provenance | Made available in DSpace on 2021-06-13T00:13:08Z (GMT). No. of bitstreams: 1 ntu-96-P94846002-1.pdf: 647121 bytes, checksum: 7ee90bc33e05dd557c726bf7ee8ee03c (MD5) Previous issue date: 2007 | en |
dc.description.tableofcontents | 第一章 前言 1
第二章 文獻探討 2 第一節 高密度膽固醇之高低與缺血性腦中風發生的關係 2 第二節 建立一與疾病預後相關之生物標記的適當切點值 17 第三節 效用測量 21 第三章 材料及方法 24 第一節 研究架構及流程 24 第二節 效用測量 26 第三節 實際資料之電腦模擬 32 第四節 最佳臨界切點模式的建立 36 第四章 研究結果 40 第一節 效用測量的結果分析 40 第二節 最佳臨界切點模式的結果分析 66 第三節 考量多變量因子最佳切點值之決定 75 第五章 討論 81 第一節 臨床意義 82 第二節 方法學之考量 83 第三節 研究限制 83 第六章 結論 85 參考文獻 86 附錄一 89 Manuscript: Utility Measurement of Misclassification of Screening for Chronic Disease: An Example of High-density Lipoprotein Level and Cerebral Infarct 附錄二 99 Manuscript: Determination of Optimal Cut-off Points for Interval-based Biomarkers with Bayesian Minimum Cost Decision Rule: An illustration with High-Density Lipoprotein for Screening | |
dc.language.iso | zh-TW | |
dc.title | 以效用為基礎之敏感度與特異性決定生物標記之適當切點值 以高密度膽固醇及缺血性腦中風為例 | zh_TW |
dc.title | Optimal Cut-off Point for Interval-scaled Biomarker with Consideration of Utility of Sensitivity and Specificity: An Illustration with HDL and Stroke | en |
dc.type | Thesis | |
dc.date.schoolyear | 95-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 張淑惠,楊銘欽,劉宏輝,陳威宏 | |
dc.subject.keyword | 效用測量,標準博奕法,直接目測法,效用矩陣,貝氏最低成本效益法則, | zh_TW |
dc.subject.keyword | utility,standard gamble,visual analogue scale,utility matrix,Bayes’ minimum cost decision rule, | en |
dc.relation.page | 116 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2007-07-28 | |
dc.contributor.author-college | 公共衛生學院 | zh_TW |
dc.contributor.author-dept | 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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