Tumor growth and overall survival modeling to support decision making in phase Ib/II trials: A comparison of the joint and two‐stage approaches

Abstract

Model‐based tumor growth inhibition (TGI) metrics are increasingly used to predict overall survival (OS) data in Phase III immunotherapy clinical trials. However, there is still a lack of understanding regarding the differences between two‐stage or joint modeling methods to leverage Phase I/II trial data and help early decision‐making. A recent study showed that TGI metrics such as the tumor growth rate constant K_G may have good operating characteristics as early endpoints. This previous study used a two‐stage approach that is easy to implement and intuitive but prone to bias as it does not account for the relationship between the longitudinal and time‐to‐event processes. A relevant alternative is to use a joint modeling approach. In the present article, we evaluated the operating characteristics of TGI metrics using joint modeling, assuming an OS model previously developed using historical data. To that end, we used TGI and OS data from IMpower150—a study investigating atezolizumab in over 750 patients suffering from non‐small cell lung cancer—to mimic randomized Phase Ib/II trials varying in terms of number of patients included (40 to 15 patients per arm) and follow‐up duration (24 to 6 weeks after the last patient included). In this context, joint modeling did not outperform the two‐stage approach and provided similar operating characteristics in all the investigated scenarios. Our results suggest that K_G geometric mean ratio could be used to support early decision‐making provided that 30 or more patients per arm are included and followed for at least 12 weeks.