While randomized controlled trials (RCTs) are the gold standard for evaluating the safety and efficacy of new medical treatments, maintaining a concurrent control arm is sometimes not feasible and can lead to increased patient burden and threaten the completion of a trial.
Such uncontrolled trials are commonly conducted in rare, orphan, or very serious drug indications, when there is a shortage of patients or investigational drug, when there are scientific concerns about treatment switching/crossover, or for ethical concerns. In such cases, sponsors rely on study designs that deviate from the traditional RCT, such as single-arm trials, which can yield important safety and efficacy data that can support a regulatory submission and have recognized benefits, such as smaller sample sizes, the ability to end quickly if a drug has low activity, and that all (or at least most) patients receive the investigational drug (Grayling, 2016). However, uncontrolled trials also risk generating biased data because of a lack of randomization.
To overcome these challenges, sponsors sometimes employ external controls; these improve the interpretation of single-arm trials, by providing supportive evidence that is highly contextual and would otherwise be absent, and also allow sponsors to better understand their trial population if patients were not on therapy. While there are several available external control options, the accumulation of vast amounts of patient-level data is enabling higher-quality and more informative external control arms.
This white paper discusses the concept of the Synthetic Control Arm (SCA),1 which is a type of external control that is generated using patient-level data from patients external to the trial with the goal of improving the interpretation of uncontrolled trials, which can enable better product development decisions. A series of case studies are provided to highlight the different ways a SCA has been used.