Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/49766
標題: 論廣泛性K樣本療效檢定
On General K-Sample Tests for Treatment Effect
作者: 吳宏達
關鍵字: 數學類
基礎研究
treatment effect
療效
K 樣本
正比例涉險
非正比例涉險
交互作用
局部性最優檢定
調整
穩健
加權對數秩檢定
K-sample
proportional hazards
nonproportionalhazards
interaction
locally most powerful
adjustment
robust
weighted log rank test
摘要: 在非正比例涉險的情形下,要檢定「療效」是否存在並不容易,尤其是當要比較K(>2)組樣本的時候。文獻上,有好幾種非正比例涉險模型可用;但它們可能得到不同的統計顯著性。本研究利用一個相當廣泛的相乘性涉險模型,當它滿足某些邊界條件時,可以在K 組樣本的情形下包住數種非正比例涉險模型,使得它們具有相同的加權對數秩檢定(weighted log-rank test)統計量。而這個廣泛性涉險模型是具有一廣義交互作用項的,可以用來描述時變療效與異質性效應。我們以一線性「轉換泛函」(transfer functional)求得局部性最優檢定。當資料具有其它多個解釋變數須調整、或者當模型有誤設情形(misspecification) 須有穩健之檢定量時,本研究均可導出相對應之加權檢定。最後,我們將以臺灣某醫學中心之肺癌資料為例作說明。
Treatment effect is difficult to be tested under nonproportional hazards. This isparticularly true when there are K (>2) treatment arms. In the literature variousnonproportional-hazards (NPH) models can deal with this problem, but they may leadto different results in terms of statistical significance. This study focuses on a class ofmultiplicative intensity model which include a general interaction term capable ofcapturing time-varying effect and heterogeneity effect. By imposing some boundaryconditions for the K-sample setting, several nonproportional-hazards models can beviewed as special cases and thus share common structure in K-sample weighted logrank tests. That is, different NPH models produce the same test. Locally mostpowerful tests are derived via linear transfer functional in this study. Covariateadjustment and robustness to model misspecification are discussed. Finally, a set oflung cancer data collected from a medical center of Taiwan will be analyzed forillustration.
URI: http://hdl.handle.net/11455/49766
其他識別: NSC99-2118-M005-005
文章連結: http://grbsearch.stpi.narl.org.tw/GRB/result.jsp?id=2123019&plan_no=NSC99-2118-M005-005&plan_year=99&projkey=PA9907-2441&target=plan&highStr=*&check=0&pnchDesc=%E8%AB%96%E5%BB%A3%E6%B3%9B%E6%80%A7K%E6%A8%A3%E6%9C%AC%E7%99%82%E6%95%88%E6%AA%A2%E5%AE%9A
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