Abstract
With rapid advances in understanding of human diseases, the paradigm of medicine shifts from “one-fits-all” to targeted therapies. In targeted therapy development, the patient population is thought of as a mixture of two or more subgroups that may derive differential treatment efficacy. To identify the right patient population for the therapy to target, inference on treatment efficacy in subgroups as well as in the overall mixture population are all of interest. Depending on the type of clinical endpoints, inference on a mixture population can be non-trivial and it depends on the efficacy measure as well as the estimation procedure. In this chapter, we start with introducing the fundamental statistical considerations in this inference procedure, followed by proposing suitable efficacy measures for different clinical outcomes and establishing a general logical estimation principle. Finally, as a step forward in patient targeting, we present a simultaneous inference procedure based on confidence intervals to demonstrate how treatment efficacy in subgroups and mixture of subgroups can be logically inferred. Examples from oncology studies are used to illustrate the application of the proposed inference procedure.
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Ding, Y., Wei, Y., Wang, X. (2020). Logical Inference on Treatment Efficacy When Subgroups Exist. 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_10
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DOI: https://doi.org/10.1007/978-3-030-40105-4_10
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