Q&A: Advantages of external control arm studies

Applying real-world data to external control arm studies for regulatory submissions can help life sciences companies bring treatments to patients.

By and Ryan M. McKenna | 4 minute read | May 13, 2021

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Enacted in 2016, the 21st Century Cures Act expanded the use of real-world data (RWD) in the regulatory approval process.1 Life sciences companies are exploring how best to use RWD, including external control arm studies to support regulatory submissions.

Using external control arm studies requires understanding how they work, potential challenges and benefits. Here are a few commonly asked questions and answers for those looking to get started:

What is the difference between a randomized control trial and an external control arm study?

The gold standard to evaluate efficacy of medical interventions is a randomized control trial (RCT). In this method, researchers randomize study participants into two groups – one that receives intervention and one that does not. However, RCTs are time consuming, costly, and in some situations, not feasible.2

In an external control arm study, enrolled patients receiving the intervention are compared to patients outside of the study.3 The external control arm could be patients who received treatment earlier (historical) or a group treated at the same time but in a different setting (contemporaneous). Because some external control arm studies use RWD that is already collected, it can be an efficient way to evaluate the impact of an intervention.

What is the best way to create an external control arm?

In some situations, it can be relatively simple to create an external control arm. For example, if patients in the intervention arm have similar characteristics to those in historical RCT data, researchers can use the historical data to create an external control arm.4 Because these data must be tightly aligned, it may only be valid for small sub-populations.5

Researchers could also use a microsimulation approach, in which models simulate disease progression, using RWD to tune parameters. These methods enable researchers to model long-term outcomes that might not be available in medical or claims data, but researchers must be rigorous in how they tune parameters and validate results.4

When the external patient population differs from the trial participants, researchers need to use more advanced methods. For example, using RWD such as linked claims and electronic medical record data, researchers could identify a large cohort of patients diagnosed with a specific disease. These patients likely represent a broader range of disease severity and treatment patterns when compared to patients with the same diagnosis enrolled in a RCT. In these situations, researchers must use statistical matching and weighting methods to find a subset of patients that mirror the intervention group. The goal of these methods can help generate balanced pools of participants and estimate a variety of treatment effects.4 This type of study is likely the most common application of external control arm studies that would be utilized in regulatory submissions.

What is an example of an external control arm study?

IBM Watson Health researchers created an external control arm for use in a label expansion study for a medical device designed to enhance bone healing in patients with fractures.5,6 Researchers created the intervention arm from a patient registry of bone fracture patients who used the device. The external control arm consisted of ‘unexposed’ patients researchers derived from the MarketScan Commercial Claims and the Medicare Supplemental Databases. The study used standardized coding schemes available in both data sources to assess and validate the primary outcome of the study.

What steps should life sciences companies take when deciding to use an external control arm?

Typically, the first step is to establish the need for an external control arm study. The best applications are in situations where it is difficult to recruit patient populations, such as when the disease or outcomes under study are rare. An external control arm study could be appropriate in other situations, such as when the original study was a single arm trial. Other examples include situations when administering a placebo would be considered unethical, such as when there is significant off-label use of the intervention, or if there is a high likelihood patients will experience treatment crossover.

The next step for researchers is to evaluate study feasibility and methodology. During this phase, it is important to determine if the intervention arm and the external control arm of the study are appropriately aligned on patient selection criteria and baseline patient characteristics. This step requires evaluating potential data sources (e.g., insurance claims, medical records, linked claims-electronic medical records, patient registries) for use as the external control arm. Researchers should look for potential sources of bias between the different data sources. They should also select a data source for the external control arm that enables equal ascertainment of study outcomes in both arms of the study.

Sponsors also need to work closely with the relevant regulatory authority prior to regulatory filing to ensure that there is buy-in from stakeholders in advance of study execution.

The regulatory landscape for external control arm studies is new, complex and quickly evolving. Early conversations with regulatory authorities, coupled with a rigorous and well-designed external control arm strategy can help prepare life sciences organizations for this application of real-world data.

  1. https://www.fda.gov
  2. https://onlinelibrary.wiley.com/doi/epdf/10.1002/pds.4975
  3. FDA Guidance for Industry – E 10 Choice of Control Group
  4. Design and evaluation of an external control arm using prior clinical trials and real-world data (nih.gov)
  5. Synthetic and External Controls in Clinical Trials – A Primer for Researchers (nih.gov)
  6. Irwin, Debra E. PhD, MSPHa; Kelly, Kim MSb; Winer, Isabelle MPHa; Stürmer, Til MD, MPH, PhDc; Zura, Robert MDd Methodologies for Validation of Diagnoses in Real-World Data: BONES—A Case Study, Journal of Orthopaedic Trauma: March 2021 – Volume 35 – Issue – p S28-S32 doi: 10.1097/BOT.0000000000002036
  7. Mack, Christina D. PhD, MSPHa; Pavesio, Alessandra MSb; Kelly, Kim MSb; Irwin, Debra E. PhD, MSPHc; Maislin, Greg MS, MAd; Jones, John MS, MAb; Wester, Tawana RNb; Zura, Robert MDe Breaking Barriers: Studying Fracture Healing in the BONES Program, Journal of Orthopaedic Trauma: March 2021 – Volume 35 – Issue – p S22-S27 doi: 10.1097/BOT.0000000000002035