Multiple biomarkers—and an even larger number of subgroups defined by them—are often assessed for impact on efficacy or safety response using clinical trial data, with the goal of developing tailored therapeutics. A central question in these efforts is whether sufficient confidence can be obtained in a biomarker/subgroup with a clinically meaningful effect to enable key decisions including what population to target, how to design future trials, and whether to continue development. In this presentation, I will identify and focus on the key components of rigorous biomarker/subgroup identification (BSID), emphasizing approaches to meet the common challenge of multiplicity, which impacts not only testing but also estimation of biomarker/subgroup effects. I will highlight a number of methods in the very recent literature that improve BSID to enable development of tailored therapeutics.