Bayesian dynamic power prior borrowing for augmenting a control arm for survival analysis

The use of real-world data, containing data from historical clinical studies, to construct an external control arm or to augment a small internal control arm in a randomized control trial can lead to significant improvements in the efficiency of the trial, but it may also introduce bias. To mitigate the risk of potential bias arising from the heterogeneity between the external control and the internal control arms, Bayesian dynamic borrowing, which determines the amount of borrowing by similarity between the two data sources, using power prior approaches and covariate adjustment has been introduced. For binary and continuous outcomes, an approach integrating propensity score for covariate adjustment and Bayesian dynamic borrowing using power prior has been proposed. Here, we extend this approach to survival analysis with the hazard ratio as the estimand. We propose a novel approach for estimating the amount of borrowing using the empirical Bayes method based on the log-hazard ratio between external and internal controls. For inference, the approach uses Bayesian bootstrap in combination with the empirical Bayes method, covariate adjustment, and multiple imputation, taking into account all uncertainty. The performance of our approach is examined by a simulation study. As an illustration, we apply the approach to dynamic borrowing of Flatiron real-world data for CheckMate-057 study for advanced non-squamous non-small cell lung cancer. For this application, we apply multiple imputation for missing covariates and propose a computationally efficient algorithm for computing the total variance of the log hazard ratio estimate. The proposed method can be applied to other endpoints in oncology as well as to other disease areas.