Subgroup analysis is the analysis of the treatment effect within the population. In some cases, the treatment effect of a new treatment might be only marginally effective for the population of the original study and a highly promising treatment effect is observed in a sub-population. A managerial decision on whether an additional investment should be made for a trial on the most promising sub-population we have observed needs to be made. However, without a careful risk/reward analysis, the decision of subgroup pursuit can be of high risk because the estimated subgroup effect size from the most promising subgroup tends to be overly optimistic. In this research, we propose a bootstrap-based measure of risk in pursuing a subgroup identified from an existing trial and study the statistical properties of the proposed measure. The measure we propose is intuitive, model-free, easy to calculate and has the potential to be generalized to some complicated situations. The proposed measure of risk can be used as a screening method and help a better-informed decision of subgroup pursuit.