Using Bayesian Evidence Synthesis Methods to Incorporate Real World Evidence in Surrogate Endpoint Evaluation

TL;DR

This study demonstrates that incorporating real world evidence into Bayesian surrogate endpoint validation enhances the accuracy and precision of treatment effect predictions in metastatic colorectal cancer.

Contribution

It introduces a Bayesian evidence synthesis approach that combines RCT and RWE data to improve surrogate endpoint validation.

Findings

Adding RWE reduces uncertainty in surrogate relationship estimates.

Inclusion of RWE improves prediction accuracy of overall survival.

Enhanced surrogate validation with combined RCT and RWE data.

Abstract

Objective: Traditionally validation of surrogate endpoints has been carried out using RCT data. However, RCT data may be too limited to validate surrogate endpoints. In this paper, we sought to improve validation of surrogate endpoints with the inclusion of real world evidence (RWE). Study Design and Setting: We use data from comparative RWE (cRWE) and single arm RWE (sRWE), to supplement RCT evidence for evaluation of progression free survival (PFS) as a surrogate endpoint to overall survival (OS) in metastatic colorectal cancer (mCRC). Treatment effect estimates from RCTs, cRWE and matched sRWE, comparing anti-angiogenic treatments with chemotherapy, were used to inform surrogacy patterns and predictions of the treatment effect on OS from the treatment effect on PFS. Results: Seven RCTs, four cRWE studies and three matched sRWE studies were identified. The addition of RWE to RCTs reduced the uncertainty around the estimates of the parameters for the surrogate relationship. Addition of RWE to RCTs also improved the accuracy and precision of predictions of the treatment effect on OS obtained using data on the observed effect on PFS. Conclusion: The addition of RWE to RCT data improved the precision of the parameters describing the surrogate relationship between treatment effects on PFS and OS and the predicted clinical benefit.