DOI: 10.25934/pr00009738
title: Translating Causal Inferences from Randomized & Single-Arm Trials to Externally-Controlled Trials

description: Randomized controlled trials (RCTs) are the gold standard of evidence for assessing if an investigational medical product is effective. RCTs randomly assign participants to different groups, which minimizes the potential for selection bias (favoring certain types of people in a study). It does so by balancing known and unknown prognostic factors (health indicators) and clinical characteristics of study participants across blinded treatment groups, thus, telling us if the treatment actually causes the changes we see in the study results. However, in medical product development programs where RCTs are not feasible or unethical, external control arms (ECA), a newly emerging methodological approach, can be leveraged to help create comparison data to determine if the treatment causes the effects we're looking at.

ECA, which can be derived from completed clinical trials or real-world settings, such as registries, electronic health records (EHRs), insurance claims, patient-generated data, have the potential to enhance the conduct, design, and analysis of clinical trials in drug development. There has been growing interest in using real world data (RWD) to generate ECAs, in particular for use in the analysis of single-arm trials (when all study participants receive the same treatment or intervention) to provide contextual information and potentially support regulatory decision-making. ECAs can also be used for sample size estimation, proof-of-concept studies, internal validation, and early termination decisions. However, without randomization, using external control arms (ECA) may have biases. To address this, the right methods and approaches to handle those biases must be used.

There are various considerations for each specific context of use of ECAs: study sample characteristics, follow-up periods, intercurrent events, endpoint assessments, amongst others. When checking the outcomes, we need to consider the quality of the data, whether it's a “hard” endpoint such a mortality or healthcare use, or a “soft” endpoint, something more subjective like how patients report feeling and functioning in their daily lives. While hard endpoints were the standard to measure in real-world data, “soft” endpoints are increasingly being assessed in RCTs to show clinical benefit on how patients feel and function in their daily lives. It's crucial to realize the importance of having the right kind of data when using external control arms to assess the outcomes of clinical trials, whether they are concrete or subjective.

Hard endpoints can practically be assessed in most RWD, whereas soft endpoints can rarely be assessed reliably and validly in claims and/or EHR data. Recognizing the need for fit-for-purpose ECA data that would be appropriate for the assessment of RCT endpoints is important. This research considers the relevance of using ECAs to analyze hard and soft endpoints. 

In addition, while the methodology for ECAs is rapidly evolving, the majority have been in oncology indications. As such, there remains a research gap in other indications where ECA may be valuable. Hence, this research seeks to utilize methodological techniques under the causal inference framework to evaluate the utility of ECAs derived from RWD and completed clinical trials in target trials emulations, using target trials of cardiovascular therapies as case examples. If the results of the target trials and externally-controlled trials are aligned, this will foster an uptick in the use of ECAs to inform regulatory decisions in the drug approval process. If differences in results are observed, understanding the contributing factors will be critical to provide valuable insights to decision makers.