Quantification of PFS Effect for Accelerated Approval of Oncology Drugs

By the accelerated approval (AA) mechanism (Code of Federal Regulations- 21 CFR 314 and 601. Accelerated Approval Rule, 1992), the FDA may grant approval of drugs or biologic products that are intended to treat serious or life-threatening diseases using a surrogate endpoint that is reasonably likely to predict clinical benefit. In oncology, progression-free-survival (PFS) is increasingly used as such a surrogate of overall survival (OS) in Phase III confirmatory trials. Improved understanding on how to deal with the PFS endpoint in trial conduct and data analysis has mitigated some regulatory concerns about this endpoint. However, a glaring gap still exists as how to determine whether the outcome from a registration trial with PFS as the primary endpoint at the time of analysis is reasonably likely to predict a clinical benefit as normally reflected through an effect on OS. Since there is no guidance on this, regulatory agencies tend to look for a compelling PFS effect coupled with an OS effect in the right direction without specification of the effect sizes and significance levels. To address this issue, we propose a synthesized approach that combines the observed OS effect and the estimated OS effect from the PFS data to explicitly test the implicit OS hypothesis at the time of primary analysis. The proposed approach is applied to hypothetical Phase III trials in metastatic colorectal cancer and adjuvant colon cancer settings using the relationships between OS effect size and PFS effect size established from historical data. Prior information on such a historical relationship is frequently cited by relevant decision makers during regulatory reviews for drug approval. However, the information is rarely fully accounted for in the actual (mostly qualitative) decision-making process. Our approach provides a simple analytic tool for deriving a more quantitative decision. It is clear that the design based on our approach may have a larger sample size than a conventional trial with PFS as the primary endpoint, but directly address the elusive OS question that a conventional PFS trial cannot, no matter how good a surrogate endpoint PFS is.