線性轉換模型對不易感受性之區間設限資料分析

機構典藏 > College of Business and Management > Graduate Institute & Department of Statistics > Thesis > Item 987654321/87683

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/87683

Title:	線性轉換模型對不易感受性之區間設限資料分析
Other Titles:	Linear transformation model for interval censoring with a cured subgroup
Authors:	李明宣;Lee, Ming-Hsuan
Contributors:	淡江大學統計學系碩士班陳蔓樺
Keywords:	區間設限;不易感受性;轉換線性模型;EM演算法;Curemodel;EMalgorithm;Intervalcensoring;Transformationmodel
Date:	2012
Issue Date:	2013-04-13 11:32:41 (UTC+8)
Abstract:	隨著生物臨床醫學研究的發展, 右設限資料分析方法在文獻中已廣為發展與應用, 區間設限資料為醫療研究收集過程中易遇到的資料, 例如病患的定期一段時間回診觀察, 未能確切知道發生事件的準確時間, 只觀察到某兩次回診時間區間中發生。此外, 在資料結構上亦會收集到不易感受性的資料, 有些被觀察者在研究期間內不會發生我們所感興趣的事件, 通常被歸類為右設限資料, 但是這些資料為確切不發生之事件資料。本篇考慮使用線性轉換模型(transformationmodel) 分析不易感受性區間設限資料, 使用EM 演算法(EM algorithm) 和牛頓迭代法(Newton-Raphsoniterationmethod) 估計參數, 並透過模擬驗證之。 There are numerous statistical methods reported for the analysis of right-censored failure time data in the past 30 years. In a medical follow-up study, additional problems arise in the analysis of interval censoring. For example, patients are observed periodically, we don''t know the exact onset time of the disease, thus the observed failure time falls into a time period. In addition, we consider a data set with two populations. Some subjects (non-susceptibility) do not become events we are interested in and some subjects (susceptibility) become events we are interested in. The non-susceptible rate (cured rate) represents a combination of cure data and survival data. This thesis considers transformation model to analysis the interval censoring data with cured proportion. The EM algorithmis developed for the estimation and simulation studies are conducted.
Appears in Collections:	[Graduate Institute & Department of Statistics] Thesis

Files in This Item:

File	Size	Format
index.html	0Kb	HTML	244	View/Open

Loading...