Evaluating treatment effect adjusting for multiple overlapping subgroups under non-proportional hazards patterns

Music City Center 

The rapid advancement of cancer immunotherapy heralds an exciting new era in cancer treatment, but also presents unique challenges in the design and analysis of clinical trials. One such challenge is the frequent discrepancy between progression-free survival (PFS) and overall survival (OS) findings. While OS remains the gold standard primary endpoint for regulatory approval in oncology trials, serving as a clinically meaningful objective measure of both safety and efficacy, PFS is often used as the primary endpoint to accelerate drug approvals for with life-threatening malignancy. The rationale is that PFS can be assessed earlier than OS, and its effects are presumed to predict future OS outcomes. However, recent trials have highlighted a disconnect between PFS and OS findings. In some instances, PFS improvements have even been associated with potential OS detriment, often manifesting as complex non-proportional hazards (NPH) patterns.

In this talk, we explore the underlying causes of these conflicting findings, particularly the NPH patterns observed in OS data, and develop a proper strategy to detect and estimate the true treatment effect in the presence of such discrepancies.