On the two-step hybrid design for augmenting randomized trials using real-world data
Abstract
Hybrid clinical trials, that borrow real-world data (RWD), are gaining interest, especially for rare diseases. They assume RWD and randomized control arm be exchangeable, but violations can bias results, inflate type I error, or reduce power. A two-step hybrid design first tests exchangeability, reducing inappropriate borrowing but potentially inflating type I error (Yuan et al., 2019). We propose four methods to better control type I error. Approach 1 estimates the variance of test statistics, rejecting the null hypothesis based on large sample normal approximation. Approach 2 uses a numerical approach for exact critical value determination. Approach 3 splits type I error rates by equivalence test outcome. Approach 4 adjusts the critical value only when equivalence is established. Simulation studies using a hypothetical ALS scenario, evaluate type I error and power under various conditions, compared to the Bayesian power prior approach (Ibrahim et al., 2015). Our methods and the Bayesian power prior control type I error, whereas Yuan et al. (2019) increases it under exchangeability. If exchangeability doesn't hold, all methods fail to control type I error. Our methods show type I error inflation of 6%-8%, compared to 10% for Yuan et al. (2019) and 16% for the Bayesian power prior.
Cite
@article{arxiv.2501.12453,
title = {On the two-step hybrid design for augmenting randomized trials using real-world data},
author = {Jiapeng Xu and Ruben P. A. van Eijk and Alicia Ellis and Tianyu Pan and Lorene M. Nelson and Kit C. B. Roes and Marc van Dijk and Maria Sarno and Leonard H. van den Berg and Lu Tian and Ying Lu},
journal= {arXiv preprint arXiv:2501.12453},
year = {2025}
}