Abstract
A lack of understanding of human biology creates a hurdle for the development of precision medicines. To overcome this hurdle we need to better understand the potential synergy between a given investigational treatment (vs. placebo or active control) and various demographic or genetic factors, disease history and severity, etc., with the goal of identifying those patients at increased “risk” of exhibiting clinically meaningful treatment benefit. For this reason, we propose the VG method, which combines the idea of an individual treatment effect (ITE) from Virtual Twins with the unbiased variable selection and cutoff value determination algorithm from GUIDE. Simulation results show the VG method has less variable selection bias than Virtual Twins and higher statistical power than GUIDE Interaction in the presence of prognostic variables with strong treatment effects. Type I error and predictive performance of Virtual Twins, GUIDE and VG are compared through the use of simulation studies. Results obtained after retrospectively applying VG to data from an Alzheimer’s disease clinical trial also are discussed.
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Acknowledgement
This manuscript was sponsored by AbbVie. AbbVie contributed to the design, research, and interpretation of data, writing, reviewing, and approving the publication. Jia Jia, and Wangang Xie are employees of AbbVie, Inc. Qi Tang is an employee of Sanofi US, Inc. and a former employee of AbbVie, Inc. We thank Richard Rode, a former AbbVie employee, for reviewing and editing the manuscript.
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Jia, J., Tang, Q., Xie, W. (2020). A Novel Method of Subgroup Identification by Combining Virtual Twins with GUIDE (VG) for Development of Precision Medicines. 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_7
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DOI: https://doi.org/10.1007/978-3-030-40105-4_7
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