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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/46994

    Title: 半母數迴歸模型對不易感受性之區間設限資料分析
    Other Titles: A Semiparametric Regression Model with Non-Susceptibility for Interval Censoring
    Authors: 陳蔓樺
    Contributors: 淡江大學統計學系
    Keywords: 區間設限資料;不易感受性資料;長期存活資料;半母數轉換模型;混合模型;interval-censoring;non-susceptibility;long-term survivor;semiparametrictransformation model;mixed model
    Date: 2009
    Issue Date: 2010-04-15 15:53:34 (UTC+8)
    Abstract: 在過去30 年中,多數文獻針對右設限事件時間作分析討論。由於醫學研究的發展, 區間設限資料在臨床試驗研究上被廣泛收集。在這樣的試驗中,每位研究對象的病人 都是週期性的被檢驗或觀察,因此我們可以視事件時間為某段確定的時間,稱之為區 間設限時間。 一般所有存活分析中都是假設事件時間是易感受性,也就是假設每事件必然發生。 在臨床試驗中,這樣的假設對長期存活資料並不適當。 Chen et al. (2007) 和 Tong et al. (2008) 針對多重區間設限資料分別提出比例勝算模型及相加效果模型之分析。在本計 畫中,我們除了探討區間設限資料亦針對不易感受性資料,建立半母數混合迴歸模型。 在醫學臨床試驗中,廣泛收集到有長期存活的區間設限資料。因此考慮不易感受性 之區間設限資料分析,建立半母數混合迴歸模型是非常重要。 A voluminous literature on right-censored failure time data has been developed in the past 30 years. Due to advances in biomedical research, interval censoring has become increasingly common in medical follow-up studies. In these cases, each study subject is examined or observed periodically, thus the observed failure time falls into a certain interval. The assumption of the analysis of survival time is that all the failure times are susceptible to the event. In clinical trials, this assumption does not adequately hold for the survival data with long-term survivors. Chen et al. (2007) and Tong et al. (2008) developed the proportional odds model and the additive hazards model, respectively, for multivariate interval-censored failure time data. In this project, we consider a general class of semiparametric transformation with non-susceptibility for regression analysis of interval-censored failure time data. It is crucial to develop the analysis of a general class of semiparametric transformation model with non-susceptibility for interval censoring.
    Appears in Collections:[Graduate Institute & Department of Statistics] Research Paper

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