| Summary: | 碩士 === 國立清華大學 === 統計學研究所 === 104 === 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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