Abstract
In personalized medicine, the patient population is thought of as a mixture of two or more subgroups that might derive differential efficacy from a drug. A decision to make is which subgroup or union of the subgroups should the drug be developed for. Interestingly, some common measures of efficacy are such that its value for a mixture population may not be representable as a function of efficacy for the subgroups and their prevalence. This chapter describes design of study that would lead to probabilistic models so that relative risk (or odds ratio) for a mixture population can be represented as a function of relative risk (or odds ratio) for the subgroups and their prevalence.
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Acknowledgments
This research is supported in part by NSF grant DMS-1007794.
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Tang, SY., Kaizar, E., Hsu, J.C. (2013). Designing Studies for Assessing Efficacy in Mixture Populations. In: Hu, M., Liu, Y., Lin, J. (eds) Topics in Applied Statistics. Springer Proceedings in Mathematics & Statistics, vol 55. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7846-1_6
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DOI: https://doi.org/10.1007/978-1-4614-7846-1_6
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