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Randomized clinical trials (RCTs) have often been considered as the gold standard in drug development, but they may not be fully powered due to limited patient population and can even lead to ethical concerns in rare disease studies. In situations like this, real-world data (RWD)/historical data can be utilized to augment or possibly serve as the control arm for the current trial. If a subset of subjects from the RWD/historical trial could be matched to the concurrent control arm subjects and they are deemed comparable following certain criteria, then pooling the matched subjects from the historical control arm and the concurrent control arm can boost the power. In this paper, we propose two matching methods of borrowing historical control data that not only balance key observed baseline covariates but also ensure the comparability of responses between the historical and concurrent controls. Close similarity in response variables among controls reduces Type I error inflation and provides further protection against unmeasured confounding bias, which is a major challenge in using RWD. Simulation studies are conducted to evaluate the empirical performance of the two matching methods in terms of Type I error rate and power, and an illustrative description of a planned study is presented.

Matching design for augmenting the control arm of a randomized controlled trial using real-world data