Causal estimators for incorporating external controls in randomized trials with longitudinal outcomes

Abstract Incorporating external data, such as external controls, holds the promise of improving the efficiency of traditional randomized controlled trials especially when treating rare diseases or diseases with unmet needs. To this end, we propose novel weighting estimators grounded in the causal inference framework. As an alternative framework, Bayesian methods are also discussed. From trial design perspective, operating characteristics including Type I error and power are particularly important and are assessed in our realistic simulation studies representing a variety of practical scenarios. Our proposed weighting estimators achieve significant power gain, while maintaining Type I error close to the nominal value of 0.05. An empirical application of the methods is demonstrated through a Phase III clinical trial in rare disease.