此篇論文主要根據 Su et al. (2008) 的做法對右設限資料進行次群組分析 (subgroup analysis)，要探討的包含 Interaction Tree 以及利用隨機森林(randon forest) 計算自變數對次群組分析的重要性，共兩種方法，我們試圖利用模擬與實際分析來觀察這些方法的表現。在實際分析中，我們分析一筆肺癌病人的資料，使用的自變數為病人的基因表現量，在自變數數量龐大的情況下，Interaction Tree 計算速度的問題便被顯現出來；在我們的想法中，決定先對自變數進行排序，篩選出前幾位邊際效應 (marginal effect) 較大的基因進行分析，從分析結果可以看見，療效有所差異的次群組確實可以經由此方法被定義出來。

In this thesis, that we adopt the subgroup analysis to right censored data depends on the method of Su et al. (2008). There are two methods that include Interaction Tree and using the random forest to estimate the
importance of each covariate for the subgroup analysis. We try to exploit simulation and real data analysis to observe the performance of them. In real data analysis, we analyze the data of the patients with lung cancer and
use their gene expression as the covariate. However, in the large number of covariate, the problem of the calculation speed of Interaction Tree is manifest.
In our envision, we decide to sort the covariate in advance and sift the front members having bigger marginal effect to analyze. In the result, the subgroup with heterogeneity of the treatment effect can be defined through this method exactly.

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