BIOSTATISTICS SEMINAR SERIES - Dynamic Prediction of Milestones for Survival Endpoints in Metastatic Solid Tumor Cancer Clinical Trials.

This month, we have Dr. Satrajit Roychoudhury, PhD, Executive Director and Head of Statistical Research and Innovation Pfizer Inc as our seminar speaker.
He has 18 years of extensive experience in working with different phases of clinical trials for drug and vaccine. Dr. Roychoudhury is an elected Fellow of the American Statistical Association and recipient of Royal Statistical Society (RSS)/Statisticians in the Pharmaceutical Industry (PSI) Statistical Excellence in the Pharmaceutical Industry Award in 2023 and Young Statistical Scientist Award from the International Indian Statistical Association in 2019.
He will be presenting on the topic: Dynamic Prediction of Milestones for Survival Endpoints in Metastatic Solid Tumor Cancer Clinical Trials.
Details from the abstract: Overall survival (OS) is the gold standard for assessing patient benefit and cost-effectiveness of new cancer drugs. However, it is often difficult to use OS as the primary endpoint in randomized clinical trials (RCTs) for patients with metastatic cancer due to multiple reasons. In recent years, progression-free survival (PFS) has increasingly been used as the primary endpoint in metastatic cancer RCTs to accelerate development. However, regulatory authorities often seek mature OS data for approval. Therefore, it is critical to determine the target time when OS data are expected to be mature for reliable statistical inference. Motivated by an advanced renal cell carcinoma (RCC) clinical trial, we develop and investigate different prediction models leveraging information from disease progression to improve target OS prediction times. We propose a multivariate joint modeling approach considering components of progression and OS and extend two models commonly used for association to be used for OS prediction. To the best of our knowledge, this is the first comprehensive statistical study exploring the prediction of OS using different levels of information on disease progression and illustrating these models using a real, complex dataset. Our findings have significant implications for OS prediction.