Subgroup analysis is frequently used to account for the treatment effect heterogeneity in clinical trials. When a promising subgroup is selected from existing trial data, a decision on whether an additional confirmatory trial for the selected subgroup is worth pursuing needs to be made. Unfortunately, the usual statistical analysis applied to the selected subgroup as if the subgroup is identified independently of the data often leads to overly optimistic evaluations. Any statistical analysis that ignores how the subgroup is selected tends to suffer from subgroup selection bias. In this dissertation, we propose two new statistical tools to evaluate the selected subgroup. The first is a risk index which can be used as a simple screening tool to reduce the risk of over-optimism in naive subgroup analysis and the second is debiased inference to answer the question of how good the selected subgroup really is. The proposed tools are model-free, easy-to-implement and adjust for the subgroup selection bias appropriately. We demonstrate the merit of the proposed tools by re-analyzing the MONET1 trial. An extension of the debiased inference method is also discussed for observational studies with potentially many confounders.