Public Workshop: Oncology Clinical Trials in the Presence of Non-Proportional Hazards


Public Workshop

Public Workshop: Oncology Clinical Trials in the Presence of Non-Proportional Hazards


Convened by the Duke-Robert J. Margolis, MD, Center for Health Policy at Duke University and supported by a cooperative agreement with FDA, this public event will bring together multi-disciplinary experts to explore and discuss approaches for addressing key challenges related to the evaluations of cancer products in the presence of non-proportional hazards. Discussion will encompass the development and application of innovative oncology clinical trial designs, alternative statistical methods for evaluating treatment effects of time-to-event endpoints, and potential outcome measures that more accurately capture treatment effect.

The development of novel cancer therapeutics is a growing field, with the potential to transform care for patients. However, there are ongoing challenges with oncology clinical trial designs and analysis of treatment effects for cancer therapies that may impact our understanding of investigational drugs.

Traditionally, randomized clinical trials for cancer products have considered time-to-event endpoints such as progression-free survival and overall survival as the primary outcome measure in trial design. One of the most common methods used to analyze these time-to-event endpoints is the Cox proportional hazard regression model. In the Cox regression model, the model assumes that the hazard ratio is constant over time – in other words, that the hazards are proportional. However, the proportionality assumption generally does not hold true in cases where there is delayed treatment effect, diminishing treatment effect, long term survival, treatment effect only in a subgroup, or the survival curves cross each other. In such cases, summarizing the treatment effect based on the hazard ratio is incorrect, and the clinical trial designs based on this assumption are likely to be under-powered to detect treatment differences.

These distinctive characteristics in the survival curves are observed in many cancer clinical trials across various diseases and drug products. To better capture and characterize these characteristics, potential new endpoints, modifications to the traditional endpoints, new statistical methods, and new clinical trial design paradigms are needed.