rdborrow: an R package for causal inference incorporating external controls in randomized controlled trials with longitudinal outcomes

Abstract

Randomized controlled trials (RCTs) are considered the gold standard for treatment effect evaluation in clinical development. However, designing and analyzing RCTs poses many challenges such as how to ensure the validity and improve the power for hypothesis testing with a limited sample size or how to account for a crossover in treatment allocation. One promising approach to circumvent these problems is to incorporate external controls from additional data sources. This manuscript introduces a new R package called rdborrow, which implements several external control borrowing methods under a causal inference framework to facilitate the design and analysis of clinical trials with longitudinal outcomes. More concretely, our package provides an Analysis module, which implements the weighting methods proposed in Zhou et al. (2024), as well as the difference-in-differences and synthetic control methods proposed in Zhou et al. (2024) for external control borrowing. Meanwhile, our package features a Simulation module which can be used to simulate trial data for study design implementation, evaluate the performance of different estimators, and conduct power analysis. In reproducible code examples, we generate simulated data sets mimicking the real data and illustrate the process users can follow to conduct simulation and analysis based on the proposed causal inference methods for randomized controlled trial data incorporating external control data.