Conditional borrowing external data to establish a hybrid control arm in randomized clinical trials

Utilizing external data from the real world, including data from historical clinical trials, has received increasing interest in drug development. The use of external data to support drug evaluation in clinical trials has mainly been through using various matching methods for baseline characteristics to form external control arms in single-arm trials or to augment control arms of randomized controlled trials in hybrid approaches. However, matching the baseline characteristics between the trial and the external subjects can only guarantee comparability on the level of baseline characteristics. Differences in outcomes between the two data sources may still exist due to contemporaneous and operational characteristics. Similarity between the outcomes in the trial control and the external subjects with similar baseline characteristics can be critical in leveraging the external subjects in the clinical trials. In this paper, a resampling method for augmenting control arms in randomized controlled trials is proposed under the conditional borrowing framework. The new method establishes empirical distributions for the hazard ratio in outcomes between the external and trial control subjects. The borrowing decision is then derived from this empirical distribution using a measure of similarity. Once the borrowing decision is established, the borrowing weights for the external subjects, based on the similarity measure, are incorporated in the weighted partial likelihood to evaluate the treatment effect. The operating characteristics of the hybrid control arm, under both the conditional borrowing and unconditional borrowing frameworks, are evaluated. Simulation is conducted to evaluate Type I error, bias, and power. An illustrative example using simulated data is also presented.