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研究生:張啟明
研究生(外文):Chi-Ming Chang
論文名稱:統計模型之電腦輔助系統於疾病預測之應用
論文名稱(外文):A Computer-Aided System for Disease Prediction with Statistical Model
指導教授:郭旭崧郭旭崧引用關係陳秀熙陳秀熙引用關係
指導教授(外文):Hsu-Sung KuoHsiu-His Chen
學位類別:博士
校院名稱:國立陽明大學
系所名稱:公共衛生研究所
學門:醫藥衛生學門
學類:公共衛生學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:英文
論文頁數:117
中文關鍵詞:預測模式電腦輔助系統模擬隨機試驗
外文關鍵詞:prediction modelcomputer-aided systemsimulationrandomized trial design
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中 文 摘 要
統計模式在流行病學或醫療領域結果預測上扮演非常重要的角色,不管預測模式如何的廣泛被使用,仍舊存在一些爭議沒有被解決,包括未被廣泛使用的存活模式在處理歷史事件資料、重複測量資料的誤用、忽視模式的診斷與驗證及沒有能力處理多階段與重複屬性的資料。
本論文主要利用SAS程式語言開發一套菜單式,適用於醫學疾病預測的電腦輔助系統,包裝成二種常用的模式系統,包括有對數回歸模式與存活分析模式、及最近常被提議的多階段模式。
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。
結論:在這份論文中,我們展示了使用已存在的統計電腦語言結合傳統的預測模式成一新的電腦輔助預測系統,在統計預測模式被廣泛使用在臨床的今日,可以提高預測品質且更具親和力。
Abstract
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.
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