External Control Arms (ECAs) are gaining a lot of attention in clinical research, particularly in rare diseases, where traditional randomized trials are often difficult to execute. Much of the discussion focuses on the statistical methodology and study design required to identify appropriate populations and data sources. But in practice, one of the biggest challenges lies in the programming effort, which is equally critical, but often more complex than anticipated.
Given that ECAs are still an evolving area, formal regulatory and industry guidance remains relatively limited. However, available publications are beginning to address key considerations. For example, the FDA’s Data Standards for Drug and Biological Product Submissions Containing Real-World Data (2024) provides recommendations on preparing and submitting RWD-derived datasets, while highlighting challenges in standardization and traceability. In parallel, industry initiatives such as the PHUSE white paper on Data Standards for Non-Interventional Studies outline common data standardisation challenges and practical approaches to address them. In addition, dedicated working groups within PHUSE are actively contributing to the development of best practices for ECAs.
This article focuses on the practical challenges from a programming perspective, drawing on recent case study experience.
Working with real-world and heterogeneous data
From a programming perspective, ECAs differ significantly from traditional clinical trials. Instead of working with well-structured datasets collected under controlled protocols, programmers are required to integrate data from multiple sources, including Real-World Data (RWD), historical trials, observational studies, and natural history cohorts. Each source brings its own structure, conventions, and limitations, often with poor documentation.
In one case study, external control data was derived from two independent natural history cohorts across different regions. While both sources represented similar patient populations, differences in baseline definitions, visit schedules, and outcome assessments required careful reconciliation.
The programming team aligned key covariates, including baseline age, genetic subtype, and functional scores to support comparability with the treated trial population. This went far beyond standard data mapping and required informed decisions to standardize variables that were not originally designed for cross-study integration.
Harmonization and data standardization
Once data sources are understood, harmonization becomes a critical step. The validity of an ECA depends on ensuring consistent definitions across baseline variables, endpoints, covariates, and visit timing.
In practice, this involves standardizing baseline windows, assessment schedules, coding dictionaries (such as MedDRA, across multiple versions, and laboratory standard units), endpoint derivations, and covariates used for matching. Across the case studies, this proved to be one of the most time-intensive phase.
Even small differences required careful reconciliation. For example, the same functional score was recorded on different scales across studies, requiring re-derivation into a common format.
If not addressed early, these inconsistencies can significantly impact downstream analyses, including propensity score modelling and bias estimation. Early and systematic harmonization is therefore essential to ensure consistency and minimize rework.
CDISC alignment, missing data, and analytical complexity
For studies intended for regulatory submission, alignment with CDISC standards (SDTM and ADaM) is essential. However, external datasets are rarely structured with these standards in mind, requiring substantial programming effort during transformation.
In another case study, SDTM datasets pooled from multiple studies, were used as the source. However, inconsistencies in specifications and differences in SDTM Implementation Guide versions across studies created challenges in standardization and traceability during ADaM specifications development. Key variables including demographics and baseline characteristics such as age, sex, education, genotype, and clinical scores had to be consistently derived and validated across studies. Maintaining traceability was critical, with define.xml playing a key role in documenting transformations and assumptions.
At the same time, missing and inconsistent data remain inherent challenges. In the natural history cohort example, gaps in timepoints and patient coverage, limited direct comparability with the treated trial arm. Programmers addressed this by defining analysis windows and deriving aligned time variables, enabling more meaningful longitudinal comparisons. However, such adjustments introduce assumptions that must be clearly justified and documented in specifications and Reviewers guide.
ECA analyses also rely heavily on advanced statistical techniques, including propensity score matching, weighting, and longitudinal modelling. These methods can be computationally intensive, particularly when working with multiple heterogeneous datasets. In one case study, certain models required several hours to run for a single output, directly impacting timelines for quality control and iterative revisions.
As a result, programmers must optimize code for long-running processes, manage runtime constraints, and ensure reproducibility across environments. For example, when generating figures based on many simulations (e.g., 500,000 iterations), a single output could require several hours of execution time. To improve efficiency, figure generation was separated into independent programs rather than being combined within a single workflow, which significantly reduced total runtime. Similarly, validation procedures for computationally intensive simulations were performed in a staged manner, starting with smaller sample sizes and progressively increasing to the full scale, allowing for earlier detection of discrepancies, while minimizing unnecessary computational cost. In addition, parallel execution strategies were employed, with multiple programmers running processes concurrently, further reducing overall turnaround time.
Furthermore, the inherent uncertainty in external data typically necessitates multiple sensitivity analyses, requiring flexible and efficient programming workflows.
Operational constraints and regulatory expectations
Beyond technical challenges, ECAs introduce operational complexities. External datasets are often subject to strict privacy and governance requirements, with analyses conducted in secure or third-party environments. These constraints can limit direct data access, slow iteration cycles, and introduce additional layers of review and approval.
Programmers must therefore adapt to restricted computing environments, limited data visibility, and evolving access rules, all of which require careful planning to maintain timelines.
At the same time, regulatory expectations remain high. While agencies are increasingly open to ECAs, they require strong evidence of data quality, bias mitigation, and endpoint consistency. From a programming perspective, this places significant emphasis on transparency and documentation.
All transformations and analytical decisions must be fully traceable and clearly justified, including mapping approaches, imputation methods, endpoint derivations, harmonization decisions, and sensitivity analyses. Well-structured documentation is therefore as critical as the datasets themselves in supporting reproducibility and regulatory review.
Final takeaways
The development of ECAs extends far beyond data integration. It requires a structured and methodical programming approach to ensure consistency, traceability, and regulatory readiness.
The case studies highlight that successful ECA implementation depends not only on methodological rigor but also on the quality of data preparation and standardization. Early harmonization, robust documentation, and flexible programming frameworks are essential to delivering reliable and submission-ready results.
As ECAs continue to gain traction, programming plays a central role in bridging diverse data sources and generating credible evidence for regulatory decision-making. Despite the availability of industry white papers and broader guidance on observational data standardization, dedicated standards and detailed guidance specific to ECAs remain limited, highlighting the need for continued collaboration and development in this area.
Interested in learning more?
Join Gautham Selvaraj, Ralf Koelbach, and Steven Ting for their upcoming webinar, “Implementing External Control Arms in a Rare Disease Case Study” on April 30 at 10 am ET, where they will offer practical insights and experience-based strategies for implementing ECAs with real-world data:
