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Think Tank

UBC, a leading provider of pharmaceutical support services, convened a virtual gathering of experts for an invigorating discussion on the use of external comparators in clinical research and post-marketing programs. This discussion lead to the creation of an External Controls in Research Series that will be released in segments, whereby our experts respond to key questions relating to the what, why, and how of the use of external comparators.

The discussion included a group of experienced biopharmaceutical development professionals including epidemiologists, statisticians and data scientists exploring essential considerations in generating evidence on the safety and efficacy/effectiveness of treatments using external comparator groups in the study design.

In this External Controls in Research Series, we will be exploring various topics such as the historical decisions of regulators in accepting results from studies using external comparators; what factors should be considered when selecting optimal data source(s) to identify patients for the comparator group; and the future of external comparators in drug development. Comparing outcomes of patients in an appropriate external comparator cohort with outcomes of the study population allows researchers to accelerate evidence generation and achieve a range of clinical development objectives.

We begin Segment One by asking:

What is an external comparator and how are these being used in research?
Aaron_Berger

Aaron Berger, Senior Director Real World Evidence

Let us first start with a review of definitions to establish context for the rest of our conversation.

For our discussion today we are going to use the term “external comparator,” however you will also hear this concept being referred to as a “external control” or “synthetic control.” An external comparator is a cohort of patients, often assembled from real world data sources outside of a prospective investigational study (such as a randomized, controlled trial), which is used to compare to a cohort of patients who participated in an investigational study. The external comparator cohort is designed to mimic as closely as possible the characteristics of the patient cohort from the investigational study to which it is being compared. Furthermore, because these external patients may come from real world data, they typically are treated with standard of care therapies in usual clinical practice. The term “study population” will be used to refer to patients who have received the therapy under investigation.

In understanding why an external comparator might be selected instead of a traditional comparator such as placebo control or standard of care within a clinical trial, it is important to consider the limitations and challenges that can be associated with traditional comparators. For example, in life threatening illnesses with limited or no treatment options, it may be considered unethical to conduct a study with a placebo control arm. In rare diseases, small patient populations and reluctance to enroll in randomized studies are factors that would impair the achievement of patient enrollment and sufficient statistical power in studies designed with traditional controls. For some rare diseases, patients may be eager to enroll in a study of a new investigational product thereby limiting the number of patients available for a comparator arm. In this situation, those patients remaining in a comparator arm may be very different in demographic characteristics and disease presentation, thereby introducing potential bias.

Furthermore, the digitization of healthcare data has created a rich landscape of high-quality healthcare databases. Taken in combination with recent technological advancements in data interoperability, we can now assemble highly customized external comparator data sets that contain the healthcare experience of patients with characteristics that closely replicate those of the study populations. These customized comparator datasets provide an efficient approach in clinical research settings that would have previously only used a traditional comparator.

Please join us for the next segment where we will be answering: What are some examples of regulators accepting data generated from studies using external comparators?

UBC epidemiologists, clinicians, and biostatisticians support the design and execution of modernized solutions that generate evidence on the safety and effectiveness of biopharmaceutical products. For more information get in touch with our team here.