An enhanced active region finder method to find subsets with large treatment difference for high dimensional data(Session 4a)

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概要

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• From precision medicine point of view, it is an interesting theme to search for some subsets with large treatment difference between test drugs and placebo based on patient background information. Many methods such as classification and regression trees (CART [2]) and active region finder method (ARF [1]) can be used to find subsets impacted on response variable. However, these methods evaluate only influence on response variable and they don't look a treatment difference. Therefore, it is necessary to develop methods to find some subsets based on the treatment difference information. In addition, there is difficult common issue of course of dimensionality when a subset is identified on high dimensional explanatory variable space. In this paper, we proposed two methods. One is a revised method of ARF to search for the subsets with measuring treatment difference directly. The other one is a combination method of ARF and relative projection pursuit (RPP [4]) to find the subset with the largest treatment difference on 1-dimensional reducing space from raw high dimensional space. From the results of simulated data analysis with our methods, we showed that our methods could detect the subset with largest treatment difference as designed.

• 2011-11-11

著者

• Mizuta Masahiro

  Hokkaido University

• Hiro Shintaro

  Pfizer Japan Inc.

関連論文

• An enhanced active region finder method to find subsets with large treatment difference for high dimensional data(Session 4a)

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