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

Abstract

Objective Traditionally, validation of surrogate endpoints has been carried out using randomized controlled trial (RCT) data. However, RCT data may be too limited to validate surrogate endpoints. In this article, we sought to improve the validation of surrogate endpoints with the inclusion of real-world evidence (RWE). Methods We use data from comparative RWE (cRWE) and single-arm RWE (sRWE) to supplement RCT evidence for the 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 antiangiogenic 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, 4 cRWE studies, and 2 matched sRWE studies were identified. The addition of RWE to RCTs reduced the uncertainty around the estimates of the parameters for the surrogate relationship. The 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 of antiangiogenic therapies in mCRC. Highlights Regulatory agencies increasingly rely on surrogate endpoints when making licensing decisions, and for the decisions to be robust, surrogate endpoints need to be validated. In the era of precision medicine, when surrogacy patterns may depend on the drug’s mechanism of action and trials of targeted therapies may be small, data from randomized controlled trials may be limited. Real-world evidence (RWE) is increasingly used at different stages of the drug development process. When used to enhance the evidence base for surrogate endpoint evaluation, RWE can improve inferences about the strength of surrogate relationships and the precision of predicted treatment effect on the final clinical outcome based on the observed effect on the surrogate endpoint in a new trial. Careful selection of RWE is needed to reduce risk of bias.

A Framework for the Definition and Interpretation of the Use of Surrogate Endpoints in Interventional Trials’

“…8,18 Methods for the statistical validation of surrogates and examples of multidimensional frameworks evaluating the level of evidence for surrogate endpoint validity are described elsewhere. [19][20][21][22][23] In conclusion, our results have important implications for the future use of surrogate endpoints in the design, interpretation, and reporting of interventional trials. Determining what is (and what is not) a surrogate endpoint can be challenging and dependent on the framing within trials.…”