Real-World Evidence

An external control arm aims to replicate a control group in a randomised controlled trial by providing a standard of care arm using real-world data.

An an external control arm, the control cohort derived from real-world data is matched to the clinical trial protocol using demographic and clinical characteristics. Advanced statistical analyses are then performed to derive the treatment effect, where bias is minimised and quantified through rigorous methodological and statistical techniques.

While randomised controlled trials remain the accepted gold standard in validation of causal relationships, external control arms can provide valuable comparative evidence earlier in the clinical development pipeline, providing early-stage clinical efficacy to support clinical trial planning, and regulatory submissions when used to supplement single-arm Phase II trials. They are also vital in rare diseases, where standard care is suboptimal or where treatment options are limited. In these cases, an external control arm can accelerate recruitment and provide critical evidence to bring effective and much needed therapeutics to patients.

One of the key challenges with using real-world data for external control arms is often differences in the type and frequency of data collected in routine care relative to the highly controlled setting of clinical trials. Through cutting-edge data enhancement methods, Arcturis can bridge the gap between real-world data and clinical trials to enable high-quality regulatory submissions.

Multiple Myeloma case study

Observational studies at Arcturis have a number of benefits over traditional methods.

Long-running research contracts with our data partners allows us rapid access up-to-date data, and our research collaborations bring both rationale for what is seen in the data and expert clinical perspective of the research question.

Our approach can be used for a range of studies from natural history of disease and burden of illness to specific studies for value demonstration and regulatory submissions.

Natural History Studies:

Natural history studies aim to provide context to specific patient cohorts, including the combination of different treatment regimens and their sequencing, understanding natural treated progression, change in biomarkers over time, and the estimation of key clinical outcomes, both in the wider cohort, but also clinically relevant sub-groups.

Using rich longitudinal real-world data, Arcturis can provide detailed patient characterisation, along with comprehensive patient pathway analyses to understand these complex clinical pathways.

Prognostic modelling:

Using our real-world data platform, Arcturis can uncover underlying patterns in how a disease will progress and in turn, develop outcome-specific prediction models using various statistical and machine learning methods. These approaches can be used to identify “high risk” patients, to provide decision support in clinical practice and to support research staff to identify patients suitable for a clinical trial at an early stage.

Chronic Kidney Disease case study

Patient stratification:

Arcturis can apply clustering techniques to stratify patients into distinct sub-groups based on either the presence or absence of specific clinical observations or similarities with respect to outcomes. These insights can be used to improve our disease understanding, tailor therapeutic interventions, and accelerate the development of novel therapies.

THEMIS case study

Use of suboptimal patient selection criteria is one of the main causes of high failure rates of clinical trials.

Arcturis supports our partners early in the clinical trial design stage by validating and optimising inclusion and exclusion criteria to identify the most suitable patients and to reduce overall trial costs.

By mapping intended patient selection criteria of a clinical trial to our real-world data, Arcturis can validate the impact of each individual criterion on KPIs such as the number of eligible patients and trial duration. Further, our analytical approaches can uncover alternative criteria to identify “high risk” patients, who would benefit most from a new therapy, or to decrease patient variability within a trial’s target population to increase the overall probability of success.

THEMIS case study

Using our real-world data platform, Arcturis can provide insights into demographics of clinical trial target populations.

These can be used to support and guide clinical trial protocol design by evaluating all the patient selection criteria to reduce unintended bias and to increase overall diversity of the population at the design stage.

Under-served groups in clinical research can be characterised by demographic, socio-economic, health status, and disease-specific factors. These factors are highly context specific and driven by the disease area, the research question, and the target population to be studied.

Exclusion of under-served groups from clinical trials limits the understanding of the risk/benefit balance of a new therapy and reduces the diversity of clinical trial results. Consequently, safety and efficacy concerns, as well as lack of evidence relating to delivery of treatment modalities in certain groups can become barriers to uptake when a therapy is launched.

These issues are recognised by regulatory bodies such as the EMA, which is running a clinical trials transformation initiative, and the FDA, which encourages the industry to submit a diversity plan before the start of a clinical trial, both with the aim of increasing access to under-served groups.