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. 2011 Jan 30;1(8):42-55.
doi: 10.1016/j.stamet.2009.05.003.

Variable Selection for Qualitative Interactions

Affiliations

Variable Selection for Qualitative Interactions

L Gunter et al. Stat Methodol. .

Abstract

In this article we discuss variable selection for decision making with focus on decisions regarding when to provide treatment and which treatment to provide. Current variable selection techniques were developed for use in a supervised learning setting where the goal is prediction of the response. These techniques often downplay the importance of interaction variables that have small predictive ability but that are critical when the ultimate goal is decision making rather than prediction. We propose two new techniques designed specifically to find variables that aid in decision making. Simulation results are given along with an application of the methods on data from a randomized controlled trial for the treatment of depression.

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Figures

Fig. 1
Fig. 1
Plots demonstrating qualitative and non-qualitative interactions
Fig. 2
Fig. 2
Plots demonstrating usefulness factors of qualitative interactions
Fig. 3
Fig. 3
Plots of interaction variables selected from Nefazodone CBASP trial data comparing the combination treatment to Nefazodone alone. In each plot x-axis is the variable number given in Table 3, and y-axis is adjusted percent of time the variables were selected by the method. Dashed horizontal line is the 80% threshold and solid horizontal line is the 90% threshold. In the second plot the × identifies the Obsessive Compulsive Disorder variable, whereas the × in the third and fourth plots denotes Alcohol Dependence
Fig. 4
Fig. 4
Plots of interaction variables selected from Nefazodone CBASP trial data comparing the combination treatment to CBASP alone. In each plot x-axis is the variable number given in Table 3, and y-axis is adjusted percent of time the variables were selected by the method. The dashed horizontal lines are 80% thresholds.

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