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研究生:周楷沛
研究生(外文):Chou kai-pei
論文名稱:以半參數為主之隨機模式於三階段疾病進展評估
論文名稱(外文):The Semi-parametric Stochastic Model for Assessing Three-state Disease Progression
指導教授:張淑惠張淑惠引用關係陳秀熙陳秀熙引用關係
指導教授(外文):Shu-Hui ChangHH Tony Chen
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:流行病學研究所
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:2001
畢業學年度:89
語文別:英文
論文頁數:72
中文關鍵詞:半參數方法隨機過程區間設限半參數馬可夫模型
外文關鍵詞:Semi-parametric methodStochastic processinterval-censoredSemi-Markov model
相關次數:
  • 被引用被引用:0
  • 點閱點閱:363
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  • 下載下載:0
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目的:
應用半參數的方法來估計多狀態隨機模式轉移的狀況並沒有被深入的研究。因此本研究主要的目的在於發展半參數隨機過程模型來評估相關共變數如何影響三階段疾病轉移。
方法:
在病理學上本研究利用隨機過程加入比例風險形式(proportional hazard form)及部分概式函數(partial likelihood)來處理危險因子不同狀態的轉移。我們將部分概式函數(partial likelihood)推廣至適應非馬可夫假設及使用隨機效用(random effect)模式解決不同狀態間轉移之相關性。另外,我們也採用了半馬可夫(semi-Markov)的方法來將三階段隨機模式以解除馬可夫鏈停留時間分佈(Embedded Markov Chain holding time distribution) 來表現,同時考慮競爭死因(competing risk)之問題。最後,我們也發利用排列的方法和區間設限的方法來處理因隱沒狀態(hidden state)轉移而產生的問題。
兩個例子:
1.Adenoma-invasive carcinoma-death是完全知道轉移時間的資料
2.Normal-the PCDP-clinical of breast cancer是有hidden state的資料。
結論:
本研究發展半參數隨機過程模型來估計危險因子如何影響三階段疾病轉移,並考慮不同困難度下其模式之適應性。

Backgorund: The application of semiparametric method to multi- state stochastic process, particularly interval-censored data with hidden transition, has not been fully addressed.
Objectives: The aim of this study was to develop a semiparametric stochastic model for assessing the effects of covariate on three-state disease progression.
Methods: The three-state stochastic process plus the proportional hazard model and partial likelihood method was applied to assess the effect of covariates on different state transitions. Two thorny issues including the relaxation of Markov assumption and the correlation between different state transitions were also tackled by the extension of partial likelihood and the application of the random-effect model. The semi-Markov method was also proposed to model the covariate effect by the embedded Markov chain and holding time distribution taking competing risk problems into account. The permuted method and interval-censored method were adopted to tackle interval-censored data with hidden state transitions in three-state stochastic process.
Illustrations: Two illustrations were given in this study, including adenoma-invasive carcinoma-death for data with the exact transition time known and normal-the PCDP-clinical progression of breast cancer phase for interval-censored with hidden state transitions.
Conclusion: The present study proposed the semiparametric stochastic model for assessing the effects of covariates on three-state disease progression taking interval-censored data type and other issues into account.

Abstract
中文摘要
Introduction
Brief Review for previous studies on interval-censored data
Method
1.Model specification
2.Semi-parametric model for data without hidden transition
3.Random effect model for correlation between the transition from sate 0 to state 1 and the transition between state 1 state 2
4.The Semi-parametric model for hidden transition
Two Examples
Discussion
Reference

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