Comparative Analysis of Treatment Selection Methods for Binary Outcome

Student: Korytova Aleksandra

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Final Grade: 8

Year of Graduation: 2017

Identification of subgroups of patients is considered to be one of the most promising solutions for studies of multiple treatment strategies. Nowadays there are several proven algorithms and methods for allocating subgroups with the maximal treatment effects. Many of them are based on the recursive construction of subgroup trees, the other use the logistic regression. This paper presents a comparison of several approaches: the Uplift modeling, the Virtual Twins method and the SIDES-method. Keywords: Subgroup analysis, Multiple treatment strategies, Decision trees

Full text (added May 28, 2017)

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