U.S. Army promotes PhD student Swiger to lieutenant colonel
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Professor David Vance awarded five-year, $2.86-million grant by NIMH
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Improving the health of older African American men in the Deep South

Tree-structured analysis of treatment effects with large observational data

Su, X., Meneses, K., McNees, P., Johnson, W.
School of Nursing, University of Alabama at Birmingham, AL, USA xgsu@uab.edu

Abstract

The breast cancer education intervention (BCEI) is a tailored psychoeducational intervention programme aiming to improve the quality of life of breast cancer survivors. Borrowing the idea of recursive partitioning and following the convention of classification and regression trees, an exploratory procedure, termed interaction trees, is proposed to understand better the differential effects of the BCEI on longitudinal quality-of-life data. The resultant tree model identifies several objectively defined subgroups: in some groups the BCEI is quite effective whereas in others it may not be. Based on the final tree structure, a permutation test is used to assess the overall treatment-by-covariate interaction. In addition, a variable importance ranking feature is facilitated via random forests of interaction trees to help to determine important effect modifiers of the BCEI.


 Link to Wiley-Blackwell Online