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In day-to-day practice, a physician uses each patient’s individual characteristics, and possibly diagnostic test results, to make a diagnosis and choose a course of treatment. Because the best treatment choice often varies from patient to patient due to their differing characteristics, routine medical practice involves personalized treatment decisions. The advent of sophisticated machines that provide high-dimensional genetic, proteomic, or other biological data for use in this process has made it much more complex, and this often is called “precision” or “personalized” medicine. In this chapter, I will discuss the simple version of personalized medicine in which one or two patient covariates or subgroups may interact with treatment. Examples will include (1) a biomarker that interacts qualitatively with two treatments, (2) an illustration of why the routine practice of averaging over prognostic subgroups when comparing treatments can lead to erroneous conclusions within subgroups, (3) a randomized trial design that makes within-subgroup decisions, (4) a phase II–III select-and-test design that makes within-subgroup decisions, and (5) a Bayesian nonparametric regression survival analysis that identifies optimal dosing intervals defined in terms of the patient’s age and disease status.