Subgroup Analyses in Oncology Trials: Regulatory Considerations and Case Examples

Subgroup analyses are assessments of treatment effects based on certain patient characteristics out of the total study population and are important for interpretation of pivotal oncology trials. However, appropriate use of subgroup analyses results for regulatory decision-making and product labeling is challenging. Typically, drugs approved by the FDA are indicated for use in the total patient population studied; however, there are examples of restriction to a subgroup of patients despite positive study results in the entire study population and also extension of an indication to the entire study population despite positive results appearing primarily in one or more subgroups. In this article, we summarize key issues related to subgroup analyses in the benefit-risk assessment of cancer drugs and provide case examples to illustrate approaches that the FDA Oncology Center of Excellence has taken when considering the appropriate patient population for cancer drug approval. In general, if a subgroup is of interest, the subgroup analysis should be hypothesis-driven and have adequate sample size to demonstrate evidence of a treatment effect. In addition to statistical efficacy considerations, the decision on what subgroups to include in labeling relies on the pathophysiology of the disease, mechanistic justification, safety data, and external information available. The oncology drug review takes the totality of the data into consideration during the decision-making process to ensure the indication granted and product labeling appropriately reflect the scientific evidence to support patient population for whom the drug is safe and effective.

Automated summary

Subgroup analyses in oncology trials are crucial for interpreting treatment effects based on patient characteristics, but using these results for regulatory decisions and product labeling can be challenging. The decision on which subgroups to include in labeling relies on the disease's pathophysiology, mechanistic justification, safety data, and external information.