External Control Arms Archives |

Building External Control Arms in Rare Disease Clinical Trials: A Programmer’s Perspective

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:

Leveraging RWE Innovations to Inform Clinical Strategy and Strengthen Healthcare Decision-Making

Real-world evidence (RWE) is no longer a supporting actor, but rather a strategic asset that should be embedded across the product lifecycle.

We now have tools that were unimaginable a decade ago: synthetic data that preserves privacy while enabling scenario modeling and early go/no‑go decisions, external control arms (ECAs) to strengthen single‑arm trials and accelerate access in high unmet need settings,
and decentralized long‑term extensions via tokenization that reduce burden while capturing 10+ years of safety and effectiveness across the patients’ real-world journey.

These innovations aren’t just “nice to have.” They are how we accelerate access to needed therapies, demonstrate value with confidence, and build submissions that stand up to today’s scrutiny.

Here, I discuss how these capabilities are reshaping clinical strategy and unlocking smarter, faster, more equitable evidence generation.

Generating synthetic data with agentic AI

Synthetic data is artificially generated data that mimics the statistical properties of real data without containing identifiable patient information. Starting with appropriate real-world data (RWD) (patient-level) or randomized controlled trial (RCT) data source(s), sponsors can use an AI-supported pipeline to generate a synthetic dataset, then assess similarities to the original data to gauge success.

Synthetic data can:

Inform early go/no-go decisions: A cost-effective approach to optimizing asset strategy before large investments by simulating expected outcomes under various scenarios in Phase I–II.
Inform CT design: Model alternative controls and sample sizes and stress-test treatment effects in a cost-effective manner.
Build privacy-preserving cost-effective ECAs: Build an ECA partially (+ RWD) or totally through a fully de-identified synthetic cohort. This is not for regulatory purposes yet, but it can inform provider and payer decisions.

RWD has its limitations: it must closely resemble real patient populations and protect patient privacy, and can be costly, time-consuming, and potentially unethical. Synthetic data can help overcome these challenges.

Strengthen regulatory submission with an external control arm

External control arms use data from historical RCT or RWD when randomization is not feasible or ethical, or to power / accelerate a study where there is high unmet need.

ECAs can:

Strengthen single-arm trials (SAT): Provide contextual information for SAT regulatory submissions, increasing probability of success.
Accelerate access to needed therapies: For RCT in high unmet need (e.g., accelerated approval pathway) and/or with slow recruitment, RWD can augment the control arm.
Support a lifecycle management approach: Supports label expansions to new populations (e.g., to male breast cancer) or new lines of therapy for decisions by regulators, payers, and providers.

While RCTs are considered the “gold standard,” the FDA in 2023 wrote that “externally controlled studies may be considered” (with strong justification), while in 2025, the EMA guidance stated “in some situations, causal conclusions may be derived from a setting where the investigational medicinal product data was collected under a clinical trial protocol while the control arm was not a randomized arm in that same protocol.”

Assess long-term outcomes with long-term extension studies

Decentralized long‑term extensions for RCT assess long-term outcomes (safety and effectiveness) with or without drug provisions. The extension enables follow-up of tokenized trial patients via real-world databases or direct-to-patient data collection.

Long‑term extension studies can:

Allow for long-term follow-up: Cost-effective data collection by reducing site and patient burden while collecting key safety and effectiveness endpoints over 10+ years.
Enable earlier launch: For breakthrough therapies and high unmet need, launch can occur as soon as clinical efficacy is proven if the sponsor commits to a Phase IV study to collect long-term data.
Improve representativeness: Loss to follow-up in long-term studies can lead to confounding, and RCTs often under-represent certain populations. The shift to real-world endpoints makes the insights more relevant to decision-makers.

Key takeaways

Consider RWE as a strategic asset: Integrate RWE early and anticipate post-marketing collection of long-term data and adopt causal inference methods to protect ideals of safety and effectiveness.

Invest in robust RWD: Invest in RWD quality and governance to ensure credibility with regulators and payers.

Adopt a comprehensive strategy: Adopt flexible, hybrid evidence strategies that combine synthetic data, ECAs, and long-term real-world data collection approaches.

Ensure cross-functional readiness: Medical, regulatory, biostats, and data science must operate as one evidence engine.

Real-World Data Strategies and Challenges: Making Data Work for Your External Control Arm Study

External control arms (ECAs) are gaining popularity in comparative effectiveness studies, driven by a growing emphasis on robust evidence across disease areas and regulatory body acceptance. ECAs can provide a control group for single-arm studies, complement a larger portfolio of evidence, and enable research for rare or genetic conditions for which randomized controlled trials may be unethical or infeasible.

At the same time, real-world data (RWD) is becoming an essential foundation for building credible ECAs. RWD offers unique advantages: it reflects real clinical practice, captures diverse patient populations, and can provide data for robust treatment effects.

However, integrating data from multiple sources, such as historical trials, concurrent trials, patient registries, and cross-population datasets, requires careful methodological planning to ensure validity and regulatory acceptance.

To fully harness the value of external control arms, sponsors must ensure selected data is fit-for-purpose, index dates are aligned with trial eligibility, and rigorous statistical methods are applied to ensure comparable patient profiles. Here, we outline these three essential elements.

Choosing the right data source for your external control arm

When building ECAs, different types of external data sources have different strengths.

Historical or concurrent randomized trials

Historical or concurrent randomized trials contain systematically collected data and well-defined endpoints, following a detailed protocol. However, they often have small sample sizes, and evolving standards of care or diagnostic criteria can limit comparability over time.

Electronic health records and insurance claims

Electronic health records and insurance claims contain large, diverse cohorts and broad population coverage. But they frequently lack clinical details such as out-of-hospital care and non-prescription medications.

Patient registries

Patient registries provide systematic, detailed data collection, the potential for linkage and long-term follow up. Yet they can have high missingness and over-represent healthier patients, which could reduce the overlap in characteristics with trial populations.

Selecting the best data sources should be guided by fit-for-purpose assessments. These studies include exploring the availability of key prognostic characteristics and missingness, along with practical considerations such as access and timelines.

Defining appropriate eligibility criteria and index dates

Carefully establishing index dates is critical yet challenging when incorporating an ECA. In a trial population, the index date is clearly defined as when the patient meets eligibility or is randomized. The same eligibility criteria need to be applied to ECA patients using variables in the external data source. The index date should reflect the point at which those criteria are met. Misalignment of the index date leads to specific types of selection bias, including immortal time. This bias occurs when periods during which an outcome could not have occurred are misclassified, potentially creating a false treatment benefit.

Ensuring treatment and control patients are similar

In RCTs, randomization naturally balances prognostic factors between treatment arms. ECAs, by contrast, require explicit identification and adjustment of these variables. Clinical expertise is essential for determining which characteristics matter most. Comparing the distributions of these variables between the treated versus control arm helps to assess similarity. Statistical techniques including propensity-matched controls and inverse treatment of probability weighting can improve comparability and approximate the balance achieved through randomization. Assessing pre- and post-adjustment distribution of baseline characteristics quantifies the success of the method.

Final takeaways

Overall, to fully harness the value of external control arms, three elements are essential:

Selecting fit-for-purpose data
Defining index dates that align with trial eligibility
Applying rigorous statistical methods to ensure comparable patient profiles

When executed thoughtfully, ECAs can meaningfully strengthen evidence generation and expand the possibilities for clinical research.

Interested in learning more?

Watch our on-demand webinar featuring Deepa Jahagirdar and Vartika Savarna, “Driving Credibility in External Control Arms with Real-World Data,” available now.

External Control Arms in Drug Development: Methodological and Regulatory Considerations

Drug development is growing more complex, with compressed timelines and increasingly high expectations from regulators, payers, and health systems. In this setting, external control arms (ECAs) leveraging real‑world data (RWD) are emerging as a pragmatic approach to support clinical development and downstream commercial decision‑making.

Randomized controlled trials (RCTs) remain the gold standard for evidence generation. However, in many modern development programs, traditional randomized designs are not feasible or may raise ethical concerns. Sponsors increasingly encounter situations in which:

Patient recruitment is slow, limited, or not achievable
Randomization is ethically challenging
Development costs escalate rapidly
Competitive dynamics demand accelerated evidence generation
Patient populations are small or rapidly progressing
There is a high unmet medical need

These challenges are particularly acute in oncology, rare diseases, post‑approval expansion studies, and advanced or cell‑based therapies.

What is an external control arm?

An external control arm replaces or supplements a traditional control group by leveraging data from patients treated outside the clinical trial. These patients are drawn from routine clinical practice and reflect outcomes under standard‑of‑care treatment in real‑world settings.

External controls are typically constructed using real‑world data sources such as:

Electronic health records (EHRs)
Administrative and insurance claims
Disease and treatment registries

Unlike trial data, real‑world data reflect patterns of diagnosis, treatment, and follow‑up in everyday clinical care. The foundation of a well‑designed external control study is the use of fit‑for‑purpose data that are sufficiently complete, clinically relevant, and reliable to support robust and defensible analyses.

Strategic value of external control arms

When thoughtfully designed and appropriately governed, ECAs can provide meaningful strategic benefits, including:

Shortened development timelines
Improved feasibility of clinical studies
Evidence generation in small or rare populations
Stronger value narratives for payers and health technology assessment bodies
Support for lifecycle management and label expansion strategies

Methodological considerations and risks to manage

The credibility and acceptability of an external control arm depend heavily on methodological rigor.

Key considerations include the following:

1. Study design

External control studies should be designed to closely mirror the clinical trial, including:

Alignment of inclusion and exclusion criteria
Clear definition of index date and baseline
Comparable follow‑up periods and outcome assessment windows
Consistent treatment context and line of therapy

Pre-specification of the estimand and statistical analysis plan is critical to avoid post‑hoc decision‑making.

2. Patient selection and alignment

Ensuring comparability between trial participants and real‑world patients is one of the most critical aspects of ECA design. Sponsors should:

Use transparent, reproducible cohort selection algorithms
Apply consistent definitions for key demographic and clinical variables
Assess overlap and positivity between trial and external populations
Explicitly evaluate differences in baseline characteristics

Sensitivity analyses should be conducted to quantify the impact of residual differences where appropriate.

3. Handling confounding and bias

Because external control arms lack randomization, confounding must be actively addressed. Common analytical approaches include:

Propensity score methods (matching, weighting, stratification)
Multivariable outcome regression
Doubly robust methods that combine weighting and modeling

Method selection should be driven by study objectives, data characteristics, sample size, and variable completeness and not for analytical convenience.

4. Data quality and missingness

Real‑world data are inherently heterogeneous and incomplete. Methodological plans should address:

Data provenance, completeness, and validation
Handling of missing or partially observed variables
Measurement variability across providers, systems, or data sources
Differences in assessment timing and frequency

Imputation strategies and key assumptions should be explicitly documented and tested through sensitivity analyses.

5. Outcome definition and assessment

Endpoints derived from RWD must be clinically meaningful and aligned as closely as possible with trial definitions. Considerations include:

Use of validated real‑world endpoint definitions
Clear attribution and timing of outcomes
Consistency with regulatory‑recognized measures of clinical benefit
Avoidance of surrogate endpoints unless scientifically justified

Outcome misclassification remains a key risk and should be explicitly evaluated.

6. Sensitivity and robustness analyses

Regulators expect evidence that findings are robust under alternative assumptions. Analyses may include:

Variation in matching or weighting specifications
Alternative cohort definitions or look‑back periods
Use of negative control outcomes or exposures
Quantitative bias analyses where feasible

The objective is to demonstrate that conclusions are not driven by a single design or modeling decision.

7. Transparency and documentation

Methodological transparency is essential for regulatory and payer review. Best practices include:

Prespecifying analysis plans and decision rules
Fully documenting data sources, algorithms, and assumptions
Providing traceability from raw data to final outcomes
Enabling reproducibility of key analyses

Regulatory outlook and expectations

Regulatory agencies and health technology assessment bodies, including the U.S. Food and Drug Administration (FDA), the European Medicines Agency (EMA), and the Canadian Agency for Drugs and Technologies in Health (CADTH) have recognized the potential role of external control arms under conditions of methodological rigor and transparency.

Regulatory agencies have not lowered evidentiary standards. Rather, they have:

Provided greater clarity on scenarios in which external control arms may be acceptable
More explicitly articulated methodological expectations
Encouraged early and proactive dialogue with sponsors

Successful regulatory submissions that incorporate ECAs typically:

Provide a clear scientific and ethical rationale for why randomization is not feasible or appropriate
Use high‑quality, fit‑for‑purpose real‑world data sources
Transparently define patient selection criteria and demonstrate alignment with the trial population
Show that findings are robust, reproducible, and minimally biased

Early engagement with regulators remains critical to aligning expectations and maximizing the likelihood of success.

Join Anupama Vasudevan and James Matcham on February 3 at 10 a.m. ET for an open office hours on “Evidence Generation with External Control Arms”:

A Look Ahead for Biopharma: Embracing Complexity in 2026

Looking back, 2025 was a year of adjustment for biotech — instability shook an already fragile biopharma sector, AI gained significant momentum, the ability to merge open-source code with commercial software matured, and the use of real-world data (RWD) continued to increase. Most companies responded responsibly: pilots, internal capability builds, exploratory regulatory dialogue, and, unfortunately, many had significant layoffs.

2026 is poised to be about embracing complexity — it’s likely many of the trends begun in 2025 will continue, and in addition we’ll see more emphasis on women’s health, an increase in discussions on external control arms, and a spotlight on biostats/clinical pharmacology. From a regulatory perspective, both the US Food and Drug Administration and the European Medicines Agency are signaling the same thing: innovation is welcome, but only when it produces decision-grade evidence.

This has real implications for how drug development companies operate.

Women’s health has expanded beyond a niche category. Regulators are increasingly focused on sex-specific evidence and generalizability across all therapeutic areas. By 2026, this is no longer optional — it’s foundational. In December 2025, the FDA published guidance on studying sex differences, with recommendations to increase female enrollment and strengthen sex-specific analyses and reporting.

Smarter trial designs are now the default. Adaptive approaches, Bayesian methods, external control arms, and decentralized elements are acceptable — expected, even — but only when the assumptions are explicit and defensible. In addition, regulators are clarifying and further exploring the use of various datasets in regulatory submissions; there’s even been a few external control arms presented to regulators.

Behind all of this is a broader shift: biostatistics and clinical pharmacology are moving from support functions to strategic capabilities. Dose justification, estimands, missing data strategy, and model-informed drug development (MIDD) are now central to regulatory credibility.

For biopharma leaders, the message is clear:

For those companies that embrace complexity as the new normal, 2026 offers a powerful opportunity: faster development, clearer regulatory paths, and greater confidence — from regulators, investors, and, ultimately, patients.

Keep an eye out for these new trends in 2026:

Women’s health expands beyond a single therapeutic area

Women’s health is no longer confined to reproductive medicine or niche pipelines. Regulators are increasingly focused on sex-specific evidence and generalizability across all therapeutic areas.

In 2026, sponsors should expect:

Greater scrutiny of female enrolment and retention
Fewer waivers for sex-specific analyses
Increased innovation in areas such as menopause, endometriosis, fertility, and autoimmune disease

Takeaway: Women’s health isn’t just a therapeutic area; it’s a generalizability requirement. If your statistical analysis plan can’t speak clearly about sex effects (or justify why not), expect pointed questions.

External control arms move from optional to strategic

External and synthetic control arms have crossed an important threshold. In 2026, they are no longer seen as novel add-ons but as intentional design choices, particularly in oncology, rare disease, and high unmet-need indications.

Regulatory acceptance is evolving and both the EMA and FDA are leaning in — carefully:

EMA is actively developing a reflection paper on external controls (including RWD-derived arms) to shape consistent scientific expectations.1
EMA is also doubling down on DARWIN EU, its federated RWE network, with plans to extend work beyond 2027 (tender activity flagged for the first half of 2026).2
FDA continues to expand its RWE framing across programs (and sponsors are expected to demonstrate data relevance, reliability, and bias management — not just “we found a database”).3

Takeaway: External controls are not a shortcut; they’re a design choice that requires 1) pre-specified causal estimands, 2) transparent matching/adjustment strategy, and 3) sensitivity analyses that are realistic.

Biostatistics becomes a strategic capability

Clinical trial design is undergoing a quiet but fundamental upgrade. In 2026, efficiency is no longer achieved by cutting corners, but by thinking better upfront. Biostatistics is no longer a downstream function; it is becoming a strategic driver of development success.

Adaptive and Bayesian designs are becoming mainstream, particularly in early and mid-stage development. Sponsors are expected to define estimands clearly and adequately to address trial objectives, to integrate biomarkers earlier, and to design trials that answer regulatory questions — not just scientific ones. Smaller trials are acceptable; ambiguous trials are not.

In 2026:

Estimands, including novel endpoints and strategies for handling intercurrent events, and missing data strategies are key for addressing primary and secondary trial objectives and are strategic topics for assessing drug product’s risk-benefit and totality of evidence.
Bayesian methods are increasingly used not just in design, but in regulatory dialogue.
Statistics, clinical pharmacology, and translational science should continue moving closer to towards seamless integration.

Takeaway: Bringing biostatisticians to the decision-making table early and leveraging quantitative decision-making frameworks will be important for managing overall pipeline decisions.

Clinical pharmacology takes center stage

Clinical pharmacology is having a moment — and for good reason. Regulators are increasingly unwilling to forgive poorly justified dose selection, study design optimization, population enrichment, extrapolation, and/or benefit risk assessment/labelling.

MIDD is becoming the norm:

Since 2024, FDA is pushing Model-Informed Drug Development via ICH M15 draft guidance and its MIDD meeting program.4
EMA is sharpening expectations for mechanistic models (PBPK/PBBM/QSP) used in MIDD, including how they should be assessed and reported.5 A guidance is expected to be completed in 2026.
In December 2025, the FDA released a guidance that lists products that no longer need (or can reduce) 6-month non-human primate toxicity testing and, in April 2025, outlined a roadmap for reducing animal testing in preclinical safety studies.6,7

Takeaway: In 2026, there will be increased scrutiny in making sure sponsors show why the chosen dose is the best one and can justify the assumptions behind MIDD submitted, or why the approach wasn’t used.

Interested in learning more?

External Control Arms: A Powerful Tool for Oncology and Rare Disease Research

In clinical research, the randomized controlled trial (RCT) has been considered the gold standard. Yet in many areas — especially in oncology and rare diseases — running an RCT with a balanced control arm is not always possible. Patients, physicians, and regulators often face a difficult reality: how do we evaluate promising new therapies when traditional designs aren’t feasible?

This is where external control arms (ECAs) come into play. By carefully drawing on existing data sources and applying rigorous methodology, ECAs can help provide the context and comparative evidence needed to make better decisions.

Here, we will explore why ECAs are particularly valuable in oncology and rare diseases, how they support decision-making and study design, what data sources they can rely on, and which statistical methods are essential to reduce bias. We will also introduce the concept of quantitative bias analysis and conclude with why experienced statisticians are key to the success of this methodology.

Why external control arms matter in oncology and rare diseases

Oncology and rare disease research share several challenges that make traditional RCTs difficult:

Small patient populations: In rare diseases, the number of eligible patients is often extremely limited. Asking half of them to enroll in a control arm may make recruitment impossible.
High unmet need: In oncology, patients and families are eager for new options. Many consider it unacceptable to randomize patients to placebo or outdated standards of care.
Ethical constraints: For life-threatening conditions, denying patients access to an experimental therapy can be ethically challenging.
Rapidly changing standards of care: In oncology, new treatments are approved frequently. A control arm that was relevant when a trial began may become outdated by the time results are available.

In such contexts, single-arm studies (where all patients receive the experimental therapy) are common. But single-arm results alone are not sufficient. Without a comparator, how do we know if the observed survival or response rate truly reflects an advance? ECAs provide the missing context.

Even when a trial includes a control arm, unbalanced designs — such as smaller control groups or cross-over to experimental treatment — can limit the ability to make clean comparisons. External controls can augment these designs, helping to stabilize estimates and provide reassurance that results are robust.

Supporting internal and regulatory decision-making

ECAs serve multiple purposes:

Internal decision-making:
- Companies developing new therapies must decide whether to advance to the next trial phase, expand into new indications, or pursue partnerships.
- ECAs help answer questions like: Is the observed benefit large enough compared to historical data? Do safety signals look acceptable in context?
Regulatory decision-making:
- Regulatory agencies such as FDA and EMA increasingly accept ECAs as part of submissions, especially in rare diseases and oncology.
- While not a replacement for RCTs, ECAs can strengthen the evidence package and demonstrate comparative effectiveness in situations where randomization is not feasible.
Helping the medical community:
- Physicians, payers, and patients need to interpret trial results. An overall survival rate of 18 months in a single-arm study may sound promising, but how does it compare to similar patients receiving standard of care?
- ECAs help put numbers into perspective, allowing the community to better understand the true value of a new therapy.

Designing better studies with ECAs

External controls are not only a tool for analyzing results — they can also improve study design.

Feasibility assessments: By examining real-world data or prior trial results, sponsors can estimate expected event rates, patient characteristics, and recruitment timelines. This reduces the risk of under- or over-powered studies.
Endpoint selection: Understanding how endpoints behave in historical or real-world settings helps refine choices for the trial, ensuring relevance to both regulators and clinicians.
Eligibility criteria: RWD and earlier trial data can reveal which inclusion/exclusion criteria are overly restrictive. Adjusting them can broaden access while maintaining scientific rigor.
Sample size planning: By leveraging ECAs, trialists may reduce the number of patients required for an internal control arm, easing recruitment in small populations.

In other words, ECAs can shape trials from the start, rather than being seen only as a “rescue” option after the fact.

Sources of external control data

An ECA is only as good as the data it relies on. Broadly, there are three main sources:

Other clinical trials:
- Prior trials of standard of care treatments can serve as external comparators.
- Individual patient-level data (IPD) is preferred, but often only summary data is available.
- These data are typically high quality but may not perfectly match the new study population.
Published studies:
- Systematic reviews and meta-analyses of the literature can provide comparator data.
- Useful when IPD is unavailable but limited by reporting standards and heterogeneity across studies.
Real-world data (RWD):
- Sources include electronic health records, registries, and insurance claims databases.
- These capture routine clinical practice, reflecting the diversity of real patients.
- However, RWD often suffers from missing data, variable quality, and lack of standardized endpoints.

Each source has strengths and weaknesses. Often, the best approach is to triangulate across multiple sources, ensuring that conclusions do not rest on a single dataset.

The value of earlier clinical trials

Earlier-phase trials (Phase I and II) can be particularly valuable in constructing ECAs. These studies often include control arms, detailed eligibility criteria, and well-captured endpoints.

For rare diseases and oncology, earlier trials may be the only available benchmark. By carefully aligning populations and endpoints, statisticians can extract maximum value from these datasets.

The challenge, of course, is ensuring comparability. Patient populations may differ in prognostic factors, supportive care practices may evolve, and definitions of endpoints may shift over time.

This is where advanced statistical methods become essential.

Reducing bias with propensity scoring

One of the key criticisms of ECAs is the risk of bias. Without randomization, patients receiving the experimental therapy may differ systematically from those in the external control.

Propensity score methods are a powerful way to reduce this bias. The idea is simple:

For each patient, estimate the probability (the “propensity”) of receiving the experimental treatment based on baseline characteristics.
Match or weight patients in the external control group so that their distribution of covariates mirrors that of the trial patients.

This approach creates a “pseudo-randomized” comparison, balancing measured variables. While it cannot eliminate unmeasured confounding, it greatly improves fairness in comparisons.

Quantitative bias analysis: Addressing the unmeasured

Even with careful propensity scoring, unmeasured confounding remains a concern. Clinical researchers often ask: What if there are factors we didn’t account for?

This is where quantitative bias analysis (QBA) enters. QBA does not eliminate bias but helps us understand its potential impact.

For example:

Analysts can model how strong an unmeasured confounder would need to be to explain away the observed treatment effect.
Sensitivity analyses can simulate scenarios with different assumptions about unmeasured variables.

By explicitly quantifying uncertainty, QBA provides transparency. Regulators and clinicians gain confidence that conclusions are robust — or at least, that limitations are clearly understood.

The need for experienced statisticians

Constructing an ECA is not a “plug-and-play” exercise. It requires expertise across multiple domains:

Data curation: Selecting fit-for-purpose datasets, cleaning and harmonizing variables, and aligning endpoints.
Study design: Defining eligibility, follow-up time, and analysis plans that minimize bias.
Statistical methodology: Applying techniques like propensity scoring, inverse probability weighting, Bayesian borrowing, and QBA.
Regulatory communication: Explaining assumptions, limitations, and sensitivity analyses in language that regulators and clinicians can understand.

In short, ECAs demand both technical skill and strategic judgment. Partnering with experienced statisticians ensures that external controls provide credible, decision-grade evidence rather than misleading comparisons.

Final takeaways

External control arms are rapidly becoming an indispensable tool in modern clinical research — especially in oncology and rare diseases, where traditional RCTs often fall short.

They offer:

Context for single-arm studies and unbalanced designs.
Support for both internal and regulatory decisions.
Guidance in study design and feasibility planning.

By leveraging diverse data sources — from earlier trials to real-world evidence — and applying rigorous methods such as propensity scoring and quantitative bias analysis, ECAs can bring clarity and credibility to difficult development programs.

But the value of ECAs depends on how well they are planned and implemented. Done poorly, they risk misleading decisions. Done well, they empower researchers, regulators, and clinicians to make better choices for patients.

As the field evolves, one thing is clear: the expertise of skilled statisticians is the cornerstone of successful ECAs.

Interested in learning more?

Join Alexander Schacht, Steven Ting, and Vahe Asvatourian for their upcoming webinar, “Beyond the Standard Clinical Trial in Early Development: When and Why to Consider External Controls” on Thursday, October 16 at 10 a.m. ET:

External Validity Bias in HTA Submissions: A Case for Transportability Methods

Health technology assessment (HTA) bodies support decision-making for the reimbursement of new technologies at the local or national level. Recommendations made by HTA bodies are based on various sources of evidence, ranging from the preferred standard randomized clinical trials to real-world data (RWD) when trials are unavailable or not relevant to the target population of the decision problem. Non-randomized studies of treatment effects are already widely used in rare diseases and innovative technologies to contextualize findings from single-arm trials. Watch our recent webinar on real-world external control arms here.

To build trust in the evidence that supports decision making, researchers need to understand and address potential risks to study validity.

The Role of External Data in Oncology Drug Development

Randomized controlled trials (RCTs) remain the gold standard for the evaluation of the safety and effectiveness of a new treatment. However, in a number of cases alternative approaches leveraging external data (i.e., data from outside of a clinical trial) — ranging from single arm trials to augmented RCTs — can be appropriate. Here, we discuss how to leverage and incorporate external data in drug development, focusing on the use of external control arms and Bayesian borrowing.

Planning Strategies for Externally Controlled Trials: Insights from ISPOR US 2024

External Control Arms (ECAs) provide comparative evidence when recruiting patients is difficult or unethical in randomized controlled trials. ECAs have significant potential to save resources and accelerate access to innovative treatment. In a previous blog, our experts took a deep dive into the concept of ECAs, their acceptable use cases, and the current regulatory guidance.

Existing guidance on the design and conduct of externally controlled trials emphasizes the importance of early engagement with regulatory and HTA bodies to justify using an ECA and discuss the preliminary study design and statistical analyses. With the increasing use of ECAs in regulatory and HTA submissions, the acceptable use cases for ECAs and important design and analytical considerations are becoming clearer. However, sponsors still face key questions about the optimal timing to plan for an ECA and how to prepare for early interactions to address differing regulatory and HTA perspectives.

In the ISPOR US 2024 HEOR Theatre session, Jason Simeone, Evie Merinopoulou, and Grace Hsu delved into these questions, discussing how regulatory and HTA stakeholders appraise ECAs, common issues from both perspectives and proposed practical solutions. In this blog, we ask Evie follow-up questions, highlighting insights from their ISPOR HEOR Theater session.

Your ISPOR US 2024 presentation was about early planning strategies for ECA. So, what is the optimal timing to start planning for an ECA?

Ideally, sponsors that need to perform an ECA to support their development program should start planning for the ECA alongside the clinical trial design. This allows them to gain experience with current real-world data (RWD) and make any necessary investment decisions for data improvements, such as additional data collection or infrastructure upgrades. Further, considering an ECA during the trial design provides the opportunity to incorporate real-world endpoints into the clinical trial. This is particularly valuable because defining clinical endpoints in real-world databases can often be challenging, especially when they are not measured consistently between routine practice and clinical trials.

Further, we showed in our presentation how although formal guidance from regulatory and HTA bodies on ECAs is consistent, final decisions when appraising ECAs may differ. This divergence in regulatory vs HTA acceptance reflects differing requirements for ECAs. When planning ECAs, both perspectives and requirements should be considered. Therefore, within sponsor organizations, early planning is key for cross-functional alignment (between HEOR/Market Access and Medical Affairs teams) on ECA study objectives and design, leading to more efficient evidence planning. With regards to external engagements with regulators and payers, the optimal timing is very contextual, but generally, sponsors should engage with decision makers via available routes like early advice programs, early enough to have the time to incorporate feedback and adjust their RWD strategy and study design—before protocol and SAP finalization.

Is early planning necessary for all cases? For instance, if a product is being developed for an indication with a rapidly changing treatment landscape and the appropriate comparators may not yet be known, would these early planning activities still be useful?

Yes, absolutely. During the early feasibility assessments that we discussed in our ISPOR presentation, we should evaluate a range of elements to determine the feasibility of an ECA—ranging from the identification of target populations to the reliable capture of confounders and study endpoints, among other factors. Identifying relevant comparators is only one element of those assessments. Even if comparators change over time, becoming familiar with RWD and current gaps helps inform discussions about the appropriate data strategy and design, which should be flexible enough to reflect some of the changes in the treatment landscape. Perhaps now, we would want to know if treatments are well captured and elements like patient count on a relevant comparator will need to be refreshed. It is important to ask questions during the early planning stages that are specific yet broad enough to inform ECA feasibility, even if the research question evolves, particularly concerning the RWD strategy.

You’ve recommended that study sponsors should be prepared to discuss certain topics during early engagement meetings, such as the ECA rationale, data source, early design considerations, and feasibility assessment. In a resource-constrained environment, sponsors may not want to invest so much money in these activities before the very first meeting, only to receive a negative response. What topics should be prioritized for that first engagement with an HTA or regulatory agency vs. subsequent meetings?

This is an important point. Ultimately, the most crucial aspect is to clarify the justification for an ECA and assess whether the agencies are open to considering evidence from an ECA. Working with the right experts who understand agency requirements from prior experience is important. Beyond the justification for an ECA being clear, we see that most critiques of ECAs stem from data issues. So, in my opinion, presenting external data source options and discussing anticipated challenges can facilitate a more productive discussion in those early engagements. If resources are constrained, a more targeted review is sufficient rather than a full-blown data landscaping exercise.

During your presentation, you emphasized the importance of identifying fit-for-purpose data. However, in some cases, a sponsor may have to submit to an HTA body in a region where such data is not readily available. For instance, if a detailed data landscaping assessment reveals that most fit-for-purpose data is in the US, but the submission is for a European HTA agency, how can sponsors address this challenge in their submission?

First and foremost, sponsors need to present to the local agency that they have thoroughly attempted to identify a data source accurately representing the local (target) population of interest. Local agencies are usually quite understanding if sponsors can demonstrate that they made the necessary effort and did not cherry-pick data sources, but instead selected a source with the highest quality data available for the research question, in a transparent and systematic manner. However, this means that there might be some potential external validity bias that could concern a local decision-maker. For example, a UK or German payer might be concerned that evidence submitted from a US data-derived ECA may not be generalizable to the target population of the decision problem.

At Cytel, we have been engaged in some very interesting work to understand how we could adjust for this potential external validity bias using transportability methods. These are quantitative methods, similar to those adjusting for confounding, and can be reliably used to extend conclusions from one study population to an external target population. Essentially, if core evidence comes from a US-data derived ECA, transportability methods can be applied to adjust the study findings to measurable patient characteristics in the target population of interest, accounting for prognostic factors or effect modifiers. We recently published a demonstration project on this topic [1 ]. Additionally, NICE recently updated its RWE framework [2] to include transportability analysis methods.

Alternatively, sponsors could consider designing a prospective study, though this approach requires much higher costs and extensive timelines. If you’re taking this route, you should design data collection with the ECA in mind, aligning patient selection criteria, endpoint definitions, etc., which is why planning early is important.

Overall, at Cytel we encourage sponsors to approach data selection in a transparent and systematic way, as recommended across all existing ECA formal guidance documents, and leverage available analytical approaches to address potential external validity concerns when using non-local data if additional data collection is not feasible.

Which internal stakeholders should be involved in this process of early planning for ECAs, and what should sponsors consider when partnering externally?

Typically, in sponsor organizations, there are clinical development and medical affairs teams that understand regulatory requirements and processes very well. In addition, there are Market Access and HEOR/RWE teams that know RWD and real-world evidence methods very well. These teams may not always work closely together, but in our presentation, we talked about the importance of bringing these two teams together early on in planning for ECAs to align differing regulatory vs payer requirements. When selecting external partners, it’s important to work with organizations that have important methodological and technical expertise. They should also have a thorough understanding of the evolving guidance and acceptance criteria of decision-making agencies and be able to provide strategic guidance on important study design decisions and early stakeholder engagements.

Interested in exploring further? Download the slides from the ISPOR HEOR Theatre Session presented by Cytel here.

Notes

[1] Ramagopalan SV, Popat S, Gupta A, et al. Transportability of Overall Survival Estimates From US to Canadian Patients With Advanced Non–Small Cell Lung Cancer With Implications for Regulatory and Health Technology Assessment. JAMA Netw Open. 2022;5(11):e2239874. doi:10.1001/jamanetworkopen.2022.39874

[2] https://www.nice.org.uk/corporate/ecd9/resources/nice-realworld-evidence-framework-pdf-1124020816837

Quantifying Uncertainty in RWE Studies with Quantitative Bias Analysis

Missing data and unmeasured confounding are common challenges for researchers, particularly in observational studies and those involving real-world data, jeopardizing the validity of study conclusions. Here, we introduce a useful tool — quantitative bias analysis (QBA) — to address these challenges.

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Interested in learning more?

Your ISPOR US 2024 presentation was about early planning strategies for ECA. So, what is the optimal timing to start planning for an ECA?

Is early planning necessary for all cases? For instance, if a product is being developed for an indication with a rapidly changing treatment landscape and the appropriate comparators may not yet be known, would these early planning activities still be useful?

Which internal stakeholders should be involved in this process of early planning for ECAs, and what should sponsors consider when partnering externally?