| dc.contributor.advisor | 陳麗霞 | zh_TW |
| dc.contributor.author (作者) | 李涵君 | zh_TW |
| dc.creator (作者) | 李涵君 | zh_TW |
| dc.date (日期) | 2005 | en_US |
| dc.date.accessioned | 2009-09-14 | - |
| dc.date.available | 2009-09-14 | - |
| dc.date.issued (上傳時間) | 2009-09-14 | - |
| dc.identifier (其他 識別碼) | G0093354008 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/30897 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 統計研究所 | zh_TW |
| dc.description (描述) | 93354008 | zh_TW |
| dc.description (描述) | 94 | zh_TW |
| dc.description.abstract (摘要) | 當母體中有部份對象因被治癒或免疫而不會失敗時，需考慮這群對象所佔的比率，即存活分率。本文主要在探討如何以貝氏方法對含存活分率之治癒率模式進行分析，並特別針對兩種含存活分率的迴歸模式，分別是Weibull迴歸模式以及對數邏輯斯迴歸模式，導出概似函數與各參數之完全條件後驗分配及其性質。由於聯合後驗分配相當複雜，各參數之邊際後驗分配之解析形式很難表達出。所以，我們採用了馬可夫鏈蒙地卡羅方法（MCMC）中的Gibbs抽樣法及Metropolis法，模擬產生參數值，以進行貝氏分析。實證部份，我們分析了黑色素皮膚癌的資料，這是由美國Eastern Cooperative Oncology Group所進行的第三階段臨床試驗研究。有關模式選取的部份，我們先分別求出各對象在每個模式之下的條件預測指標（CPO），再據以算出各模式的對數擬邊際概似函數值（LPML），以比較各模式之適合性。 | zh_TW |
| dc.description.abstract (摘要) | When we face the problem that part of subjects have been cured or are immune so they never fail, we need to consider the fraction of this group among the whole population, which is the so called survival fraction. This article discuss that how to analyze cure rate models containing survival fraction based on Bayesian method. Two cure rate models containing survival fraction are focused; one is based on the Weibull regression model and the other is based on the log-logistic regression model. Then, we derive likelihood functions and full conditional posterior distributions under these two models. Since joint posterior distributions are both complicated, and marginal posterior distributions don’t have closed form, we take Gibbs sampling and Metropolis sampling of Markov Monte Carlo chain method to simulate parameter values. We illustrate how to conduct Bayesian analysis by using the data from a melanoma clinical trial in the third stage conducted by Eastern Cooperative Oncology Group. To do model selection, we compute the conditional predictive ordinate (CPO) for every subject under each model, then the goodness is determined by the comparing the value of log of pseudomarginal likelihood (LPML) of each model. | en_US |
| dc.description.tableofcontents | 第一章 緒論……………………………………………………………………1 第一節 研究動機………………………………………………………………1 第二節 研究目的………………………………………………………………1 第三節 文獻探討………………………………………………………………2 第四節 本文架構………………………………………………………………3 第二章 含存活分率之迴歸模式………………………………………………4 第一節 標準治癒率模式………………………………………………………4 第二節 貝氏標準治癒率模式…………………………………………………6 第2.2.1節 含存活分率之Weibull迴歸模式………………………………7 第2.2.2節 含存活分率之對數邏輯斯迴歸模式……………………………13 第2.2.3節 馬可夫鏈蒙地卡羅法與Gibbs抽樣法…………………………18 第三節 含潛在危機因子數之貝氏治癒率模式………………………………20 第三章 實證分析………………………………………………………………23 第一節 以Weibull迴歸模式為架構的治癒率模式之實證分析……………24 第二節 以邏輯斯迴歸模式為架構的治癒率模式之實證分析………………32 第三節 模型的比較……………………………………………………………40 第四章 結論與建議……………………………………………………………43 參考文獻……………………………………………………………………………44 | zh_TW |
| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0093354008 | en_US |
| dc.subject (關鍵詞) | 存活分率 | zh_TW |
| dc.subject (關鍵詞) | 貝氏治癒率模式 | zh_TW |
| dc.subject (關鍵詞) | Weibull迴歸模式 | zh_TW |
| dc.subject (關鍵詞) | 對數邏輯斯迴歸模式 | zh_TW |
| dc.subject (關鍵詞) | 完全條件後驗分配 | zh_TW |
| dc.subject (關鍵詞) | 馬可夫鏈蒙地卡羅方法 | zh_TW |
| dc.subject (關鍵詞) | Gibbs抽樣法 | zh_TW |
| dc.subject (關鍵詞) | 條件預測指標 | zh_TW |
| dc.subject (關鍵詞) | 對數擬邊際概似函數值 | zh_TW |
| dc.subject (關鍵詞) | surviving fraction | en_US |
| dc.subject (關鍵詞) | Bayesian cure rate models | en_US |
| dc.subject (關鍵詞) | Weibull regression model | en_US |
| dc.subject (關鍵詞) | log-logistic regression model | en_US |
| dc.subject (關鍵詞) | full conditional posterior distributions | en_US |
| dc.subject (關鍵詞) | Markov chain Monte Carlo method (MCMC) | en_US |
| dc.subject (關鍵詞) | Gibbs sampling | en_US |
| dc.subject (關鍵詞) | conditional predictive ordinate (CPO) | en_US |
| dc.subject (關鍵詞) | log of pseudomarginal likelihood (LPML) | en_US |
| dc.title (題名) | 含存活分率之貝氏迴歸模式 | zh_TW |
| dc.type (資料類型) | thesis | en |
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