Abstract
In contrast to the “one-size-fits-all” approach of traditional drug development, the paradigm of tailored therapeutics seeks to identify subjects with an enhanced treatment effect. In this chapter, we describe a statistical approach (TSDT) to subgroup identification that utilizes ensemble trees and resampling. For each potential subgroup identified, TSDT produces a multiplicity-adjusted strength of the subgroup finding as well as a bias-adjusted estimate of the treatment effect in the identified subgroup, both of which are important for decision-making in the development of tailored therapeutics. We describe a careful examination of simulation studies in a number of related publications, in order to determine the ideal framework to compare subgroup identification methods. A simulation study is performed to evaluate the performance of TSDT. The method has been implemented in a publicly available R package.
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Shen, L., Showalter, H., Battioui, C., Denton, B. (2020). Subgroup Identification for Tailored Therapies: Methods and Consistent Evaluation. In: Ting, N., Cappelleri, J., Ho, S., Chen, (G. (eds) Design and Analysis of Subgroups with Biopharmaceutical Applications. Emerging Topics in Statistics and Biostatistics . Springer, Cham. https://doi.org/10.1007/978-3-030-40105-4_8
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