Dynamic Prediction of the Target Survival Time in Metastatic Solid Tumor Cancer Clinical Trials

TL;DR

This paper develops and compares statistical models to predict overall survival times in metastatic cancer trials, using disease progression data to inform clinical decision-making.

Contribution

It introduces a multivariate joint modeling framework for predicting OS based on progression data, a novel approach in this context.

Findings

Models improve accuracy of OS prediction using progression information.

First comprehensive study applying joint models to complex metastatic cancer data.

Results have implications for timing of OS data maturity in clinical trials.

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 three 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.