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研究生(外文):Chi-Ming Chang
論文名稱(外文):A Computer-Aided System for Disease Prediction with Statistical Model
指導教授(外文):Hsu-Sung KuoHsiu-His Chen
外文關鍵詞:prediction modelcomputer-aided systemsimulationrandomized trial design
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中 文 摘 要
整體的架構包含資料與模式兩部分模組,從早期的資料編輯、資料管理、交互驗證所需的資料抽樣及計算新的變項如多項回歸使用的中心變項(centering variable)等;模式模組由三個模式所構成,使用於二元資料的對數回歸模式、用於歷史事件資料的存活模式、以疾病自然史與實證資料利用概似函數估計參數構成的多階段模式。每一模式模組都包括有模式的驗證、檢查及預測。
兩個慣用的模式與多階段模式應用於瑞典兩個郡的乳癌篩檢試驗及台灣多中心癌症篩檢計畫的高危險群乳癌篩檢兩個資料庫,其結果顯示這系統可有效的解決使用者的問題。多階段疾病進展模式更進一步應用在兩種模擬模式: 馬可夫世代模擬及蒙地卡蘿模擬模式,這樣的模擬經常應用在篩檢計畫成本效益的評估上,進而利用模擬結果做為實際組織性篩檢政策之依據。這套電腦輔助系統的效能評估採用隨機試驗設計。
Statistical models play an important role in outcome prediction for modern epidemiologists and clinicians. Despite the wide use of such predictive models, several problems, including unpopular use of survival models in dealing with event history data, the failure of taking correlated data into account, lack of model diagnosis and validation, and mishandling of data with multi-state and repeated property, remains unsolved.
The aim of the thesis was thus to develop a computer-aided system to combine two conventional models (logistic regression models and survival models) with a recently developed multi-state model into a menu-driven, user-friendly and SAS-based package.
The overall framework includes two parts, data and model module. The former consists of data editing, data management, sampling for splitting data in cross-validation, and the computation of new variables such as centering in polynomial regression model. The model module consists of three models, logistic regression models for binary data, survival models for event history data, and multi-state model for delineating the disease natural history and estimating parameters with likelihood function formed by empirical data. Each model module also included model validation, checking, and prediction.
The two conventional models and multi-state model were then applied to two datasets, breast cancer screening form the Swedish Two-county Trial and a separate one from Taiwan multi-centre cancer screening with this computer-aided system. The results indicated the system was an efficient tool for user to solve the problems. The multi-state model with Markov cohort and Monte Carlo approaches was used to evaluate a practical screening scheme, which is a part of the cost-effectiveness analysis of the screening program. The performance of the developed package was evaluated by a randomized trial design.
In conclusion, we demonstrated in this thesis that statistical prediction model commonly used today in the clinical world can be enhanced and made more user-friendly by combining conventional models with a new multi-state model using existing statistical computer language.
Barie PS, Hydo LJ, Fischer E. Comparison of APACHE II and APACHE III scoring systems for mortality prediction in critical surgical illness. Arch Srug 1995; 130: 77-82.
Barie PS, Hydo LJ, Fischer E. Development of multiple organ dysfunction syndrome in critically ill patients with perforated viscus. Arch Srug 1996; 131: 37-43.
Berger MM, Marazzi A, Freeman J, et al. Evaluation of the consistency of Acute Physiology and Chronic Health Evaluation (APACHE II) scoring in a surgical intensive care unit. Crit Care Med 1992; 20: 1681-1687.
Brown Michael C. MD, Crede William B. MD. Predictive ability of Acute Physiology and Chronic Health Evaluation II scoring applied to human immunodeficiency virus-positive patients. Critical Care Med. 1995; 23(5): 848-853.
Calvert W.S. Meimei, M.J. Concepts and case studies in data management. BBU Press, SAS Publishing, 1996.
Chang CM, Kuo HS, Chang SH, Chang HJ, Liou DM, Chen THH. Development and evaluation of a computer-aided disease prediction application software with SAS component language. J Eval Clin Pract 2004 (accepted).
Chen FG, Koh KF, Goh MH. Validation of APACHE II score in a surgical intensive care unit. Singapore Med J 1993; 34: 322-324.
Chen THH, Kuo HS, Yen MF, Lai MS, Tabar L, Duffy SW. Estimation of sojourn time in chronic disease screening without data on interval cases. Biometrics 2000; 56:167-172.
Chen, H. H., Duffy, S. W., Tabar L. and Day N. E.. Markov chain models for progression of breast cancer: Part I : tumour attributes and the preclinical screen-detectable phase.. Journal of Epidemiology and Biostatistics 1997; 2, 9-23.
Chouaid C, Bassinet L, Fuhrman C, Monnet I, Housset B. Routine use of granulocyte colony-stimulating factor is not cost-effective and does not increase patient comfort in treatment of small-cell lung cancer: an analysis using a Markov model. J Clin Oncol 1998; 16: 2700-2707.
Cuckle H.S., Wald N.J. & Thompson S.G. Estimating a woman’s risk of having a pregnancy associated with Down’s syndrome using her age and serum alpha-fetoprotein level. British Journal of Obstetrics and Gynaecology 1987; 94, 387-402.
D''Agostino R.B. & Pozen M.W. The logistic function as an aid in the detection of acute coronary disease in emergency patients. Statistics in Medicine 1982; 1, 41-48.
Duffy SW, Chen HH, Tabar L, et al. Estimation of mean sojourn time in breast cancer screening using a Markov Chain Model of both entry to and exit from the preclinical detectable phase. Stat Med 1995; 14(14): 1531-1543.
Edwards F.H., Albus R.A., Zajtchuk R., Graeber G.M., Barry M.J., Rumisek J.D. & Arishita G. Use of a Bayesian Statistical Model for Risk assessment in coronary artery surgery. The Annals of Thoracic Surgery 1988; 45: 437-440.
Eric Tom, Kevin A. Schulman. Mathematical models in decision analysis. Infect Control Hosp Epidemiol 1997; 18: 65-73.
Faraggi D., Simon R. A neural network model for survival data. Statistics in Medicine 1995; 14: 73-82.
Feinstein A.R. An additional basic science for clinical medicine. Annals of International Medicine 1983;99:393-397
Glanz K, Rimer BK, Lewis FM. Health behavior and health education theory: research and practice. San Francisco: Jossey-Bass, 2002.
Goldhill DR, Withington PS. The effect of case mix adjustment on mortality as predicted by APACHE II. Intensive Care Med 1996; 22: 415-419.
Headly J., Theriault D.O. & Smith T.L. Independent Validation of APACHE II severity of illness score for predicting mortality in patients with breast cancer admitted to the intensive care unit. Cancer 1992; 70: 497-503.
Hsieh HJ, Chen THH, Chang SH. Assessing chronic disease progression using non-homogeneous exponential regression Markov models: an illustration using a selective breast cancer screening in Taiwan. Stat Med. 2002; 21:3369-3382.
Jennett B. et al. Guideline for initial management after head injury in adults. British Medical Journal 1984; 288: 983-985.
Johnson M.H., Gordon P.W. & Fitzgerald F.T. Stratification of prognosis in granulocytopenic patients with hematologic malignancies using the APACHE II severity of illness score. Critical Care Medicine 1986; 14: 693-697.
Kalbfleisch JD, Lawless JF. The analysis of panel data under a Markov assumption. J Am Stat Assoc 1985; .80, 392:863-871.
Knaus W.A., Draper E.A., Wagner D.P. & Zimmerman J.E. APACHE II: A severity of disease classification system. Critical Care Medicine 1985; 13: 818-829.
Knaus W.A., Zimmerman J.E., Wagner D.P., Draper E.A. & Lawrence D.E. APACHE-acute physiology and chronic health evaluation: a physiologically based classification system. Critical Care Medicine 1981; 9: 591-597.
Knaus WA, Wagner DP, Draper EA, et al. The APACHE III prognostic system: risk prediction of hospital mortality for critically ill hospitalized adults. Chest 1991; 100: 1619-1636.
Lai MS, Yen MF, Kuo HS, Kong SL, Chen THH, Duffy SW. Efficacy of breast-cancer screening for female relatives of breast-cancer-index cases: Taiwan multicentre cancer screening (TAMCAS). Int J of Cancer 1998; 78:21-26.
Launitzen SL, Spiegelhalter D. Local computations with probabilities on graphical structures and their applications to expert systems. J of Stat. Soc. Series B, 1988; 50: 157-189
Ludwigs U. & Hulting J. Acute physiology and Chronic Health Evaluation II scoring system in acute myocardial infarction: A prospective validation study. Critical Care Medicine 1995; 23: 854-858.
McKnight B, Crowley J. Tests for differences in tumor incidence based on animal carcinogenesis experiments. J Am Stat Assoc 1984: 79: 639-648.
Mendelow AD, Teasdole G, Jennett B, Bryden J, Hessett C, and Murrary G. Risks of intracranial haematoma in head injured patients BMJ 1983;287:1173-1176
Murray GD, Murray LS, Barlow P, Teasdale GM, Jennett WB. Assessing the performance and clinical impact of a computerized prognostic system in severe head injury. Statistic in Medicine 1986; 5: 463-470.
Orr R, Col NF, Kuntz KM. A cost-effectiveness analysis of axillary node dissection in postmenopausal women with estrogen receptor-positive breast cancer and clinically negative axillary nodes. Surgery 1999; 126: 568-76.
Peter W.F. Wilson MD, Ralph B. D’Agostino PhD, Daniel Levy MD, Albert M. Belanger BS, Halit Silbershatz PhD, William B. Kannel MD. Prediction of coronary heart disease using risk factor categories. Circulation. 1998;97:1837-1847.
Robeneck L, Hartigan PM, Huang IW, Souchek J, Wary NP. Predicting outcomes in HIV-Infected Veterans: II Surviavl after AIDS. J Clin Epidemiol 1997; 50: 1241-1248.
Rocktaeschel Jens MD. Morimatsu Hiroshi MD, Uchino Shgehiko MD, Bellomo Rinaldo MD. Unmeasured anions in critically ill patients: Can they predict mortality? Critical Care Med. 2003; 31(8): 2131-2136.
Rowan KM, Kerr JH, Major E, et al. Intensive care society’s APACHE II study in Britain and Ireland—II: Outcome comparisons of intensive care units after adjustment for case mix by the American APACHE II method. BMJ 1993; 307: 977-981.
SAS/AF Software Procedure Guide, Version 8. SAS Publishing, 2000.
SAS/GRAPH Software: Reference, Version 8, Volumes 1 and 2. SAS Publishing, 2000.
SAS/STAT User''s Guide, Version 6, Fourth Edition, Volumes 1 and 2. SAS Publishing,, 1990.
Sonnenberg FA, Beck JR. Markov Models in Medical Decision Making. Medical Decision Making. 1993; 13: 322-338.
Spiegelhalter DJ, A. Philip Dawid, Tom A. Hutchinson et al. Probabilistic Expert Systems and graphical modeling:a case study in drug safety. Phil Trans R. Soc. Lond. 1991;337:387-405.
Tabar L., Fagerberg C.J., Gad A. et al. Reduction in mortality from breast cancer after mass screening with mammography. Lancet 1985; 1: 829-832.
Titterington DM, Murray GD, Murray LS, Spiegelhalter DJ, et al. Comparison of discriminate techniques applied to a complex data set of head injured patients Journal of the Royal Statistical Society Series A 1981;144:145-175
Walter SD, Day NE. Estimation of the duration of a pre-clinical disease state using screening data. Am J Epidemiol 1983; 118:865-886.
Wong D.T. & Knaus W.A. Predicting outcome in critical care, the current status of the APACHE prognostic scoring system. Canadian Journal of Anesthesia 1991; 38: 374-383.
Wong DT, Crofts SL, Gomez M, et al. Evaluation of predictive ability of APACHE II system and hospital outcome in Canadian intensive care unit patients. Crit Care Med 1995; 23: 1177-1183.
Wu HM, Yen MF, Chen THH. SAS macro program for non-homogeneous Markov process in Modeling multi-state disease progression. Comput Meth Prog Bio 2004; in press.
Zimmerman JE, Wanger DP, Knaus WA, Williams JF, Kolakowski D, Draper EA. The use of risk prediction to identify candidates for intermediate Care Unit 1995; 108: 490-449.
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