External control arms (ECAs) are patient cohorts from external data sources which are used as comparators for clinical trials. ECAs may be constituted using data from control arms from completed randomized clinical trials (RCTs) (i.e., synthetic control arms) or from various sources of real-world data (RWD). ECAs are typically  considered in instances when it is impractical or unethical to randomize patients in a trial (e.g. for single-arm trials), or in instances where the comparator included in an RCT may not be representative of treatment(s) typically used in clinical practice or where the RCT control arm is small.

ECAs have been successfully used to gain regulatory and Health Technology Assessment (HTA) approval; they also have been successfully used in post-marketing studies. While ECAs may expedite clinical development, their acceptance by regulatory, HTA, and/or other key stakeholders is conditional on the rigor of design, data, and analyses. Understanding the clinical and regulatory landscape is critical as there are a complex array of decisions to make in developing ECAs, such as the design of the trial to which the ECA will be applied, the choice of ECA data source(s), constitution of the ECA cohort, data quality, availability and quality of key outcomes, comparability of the ECA and trial cohorts, and means by which comparisons are made between the active arm and the ECA.

In this session, we will outline situations where an ECA based on RWD might be considered, share insights on key design and analytic considerations, including selection of data source(s), how to limit biases in the constitution of ECAs, and the application of appropriate statistical methods.

Learning Objectives:

  • When the use of an ECA should be considered
  • Key challenges for a robust ECA based on RWD
  • Guidance for designing and analyzing data from an RWD ECA

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