Comparing Four Methods for Estimating Tree-Based Treatment Regimes

Sies Aniek () and Iven Van Mechelen
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Sies Aniek: Faculty of Psychology and Educational Sciences, KU Leuven, Tiensestraat 102, box 3713, 3000Leuven, Belgium
Iven Van Mechelen: Faculty of Psychology and Educational Sciences, KU Leuven, Tiensestraat 102, box 3713, 3000Leuven, Belgium

The International Journal of Biostatistics, 2017, vol. 13, issue 1, 20

Abstract: When multiple treatment alternatives are available for a certain psychological or medical problem, an important challenge is to find an optimal treatment regime, which specifies for each patient the most effective treatment alternative given his or her pattern of pretreatment characteristics. The focus of this paper is on tree-based treatment regimes, which link an optimal treatment alternative to each leaf of a tree; as such they provide an insightful representation of the decision structure underlying the regime. This paper compares the absolute and relative performance of four methods for estimating regimes of that sort (viz., Interaction Trees, Model-based Recursive Partitioning, an approach developed by Zhang et al. and Qualitative Interaction Trees) in an extensive simulation study. The evaluation criteria were, on the one hand, the expected outcome if the entire population would be subjected to the treatment regime resulting from each method under study and the proportion of clients assigned to the truly best treatment alternative, and, on the other hand, the Type I and Type II error probabilities of each method. The method of Zhang et al. was superior regarding the first two outcome measures and the Type II error probabilities, but performed worst in some conditions of the simulation study regarding Type I error probabilities.

Keywords: treatment regime; subgroup analysis; recursive partitioning; personalized medicine; decision tree (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1515/ijb-2016-0068

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