On the Two-Step Hybrid Design for Augmenting Randomized Trials Using Real-World Data.

Abstract

Hybrid clinical trials, which borrow real-world data (RWD) from patient registries, claims databases, or electronic health records (EHRs) to augment randomized clinical trials, are of increasing interest. Hybrid clinical trials are especially relevant for rare diseases, where the recruitment of large sample sizes may be challenging. While these trials may better use available information, they assume that the RWD and randomized control arm are exchangeable. Violating this assumption can induce bias, inflate Type I error, or adversely affect statistical power. A two-step hybrid design first tests the exchangeability between randomized control arm and external data sources before incorporating RWD as a comparator for statistical inferences (Yuan et al. 2019). This approach reduces the chance of inappropriate borrowing but may simultaneously inflate the Type I error rate. We propose four different methods to control the Type I error rate under the exchangeability assumption. Approach 1 estimates the variance of the overall test statistic and rejects the null hypothesis based on a Z-test. Approach 2 uses a numerical method to determine the exact critical value for Type I error control. Approach 3 splits the Type I error rates according to the equivalence test outcome. Approach 4 adjusts the critical value only when equivalence is established. We illustrate these methods using a hypothetical scenario in the context of amyotrophic lateral sclerosis (ALS). We evaluate the Type I error and power under various clinical trial conditions in comparison with the Bayesian power prior approach (Ibrahim et al. 2015). We demonstrate that our proposed methods and Bayesian power prior control Type I error and increase power under the exchangeability assumption, whereas the method proposed by Yuan et al. (2019) results in an increased Type I error. In the scenario where the exchangeability assumption does not hold, all methods fail to control the Type I error. Our proposed methods, however, limit a maximum Type I error inflation ranging from 6% to 8%, which compares favorably to 10% for Yuan et al. (2019) and 16% for the Bayesian power prior. All methods increase statistical power under the exchangeability condition but may lead to a loss of statistical power when the exchangeability assumption is violated.