Subgroup analysis is designed to evaluate whether an intervention has differing effects according to baseline characteristics of participants in clinical trials. This approach can help identify subgroups of patient populations, within a single trial or across multiple trials (meta-analysis), that may benefit from the intervention or can be hypothesis-generating for future trials. Subgroups can also be defined by variables that are prognostic of clinical outcomes or predictive of better treatment effect, such as disease severity, previous therapies, genotype, and biomarker status. Using the same definition of subgroup enables comparison of outcomes between similar subgroups across different clinical trials. This review will provide why we need subgroup analysis, challenges and possible solutions and pitfalls to avoid for investigators involved in the design, conduct, or interpretation of subgroup analyses.