The following is a guest article by Ashley Daigneau, Head of Clinical Trials at Verana Health

Active-controlled trials (ACT) are becoming a backbone of modern medicine. By comparing a new therapy directly against the existing standard of care, rather than a placebo, these trials generate direct head-to-head evidence on relative efficacy and safety. It’s an approach that helps clinicians, regulators, and payers confidently determine whether a novel treatment truly advances patient outcomes or is at least non-inferior compared to current practice.

With market competition and pressure to prove substantial improvement over existing treatments, active-controlled trials are increasingly common.

A fundamental challenge arises, however, when the comparator drug—the “standard of care”—has an incomplete safety and effectiveness profile, especially in diverse patient populations.

This issue is particularly relevant for drugs approved before 2007, prior to the enactment of the U.S. Food and Drug Administration Amendments Act (FDAAA). Before FDAAA, post-marketing safety reporting requirements were less robust, and systematic collection of long-term or diverse population data was limited. As a result, many widely used therapies lack comprehensive safety data in patient subgroups defined by age, race, sex, comorbidities, or concomitant therapies.

This knowledge gap can undermine the integrity and utility of ACTs—unless it is addressed through innovative approaches, most notably by incorporating real-world data (RWD) to gain a better understanding of effectiveness and safety in wider population groups over longer periods.

Limitations of Active-Controlled Trials

ACTs are designed to answer practical clinical questions: Is a new treatment better, safer, or more tolerable than the existing standard? They often influence treatment guidelines and reimbursement decisions. Yet their reliability depends heavily on the robustness of the comparator’s safety and effectiveness profile.

When comparator drugs lack comprehensive post-market data, several challenges emerge.

With uncertain baseline risks, researchers may misinterpret differences in safety between the new and existing therapies. This also limits generalizability. Trials typically enroll carefully selected populations. Without complementary data, results may not reflect the experience of patients in routine clinical care.

Lack of broad, post-market data for comparator drugs can lead to regulatory blind spots. A new drug may be penalized for apparent risks that are, in fact, shared by the comparator but undocumented. Conversely, safety signals unique to the new therapy may be obscured by insufficient comparator data.

For ACTs of drugs or therapies with limited safety data, it becomes essential to contextualize with more comprehensive information.

The Critical Role of Real-World Data

Real-world data—drawn from electronic health records (EHRs), claims databases, patient registries, and digital health devices—offers a powerful supplement to ACT evidence.

Unlike traditional randomized controlled trials (RCTs), which are bounded by eligibility criteria, timeframes, and costs, RWD captures the breadth of routine clinical practice. And now, with artificial intelligence, particularly machine learning and natural language processing models, it’s possible to curate and harmonize unstructured RWD at scale and with unprecedented speed, unlocking significant potential.

For comparator drugs with incomplete safety profiles, RWD can provide expanded safety insights. RWD offers a window to adverse events across diverse patient populations, including groups historically underrepresented in RCTs (e.g., older adults, racial and ethnic minorities, patients with multiple comorbidities).

RWD also provides evidence of long-term effectiveness. Whereas ACTs typically measure outcomes over months or a few years, RWD allows analysis of the durability of treatment effects, adherence patterns, and effectiveness across healthcare settings over sustained periods. This is critical when comparing a new therapy against an older one that has decades of real-world usage but limited trial evidence.

Incorporating RWD into ACT design or post-hoc analysis provides benchmarks for expected rates of adverse events or treatment discontinuation. This allows for more accurate attribution of differences observed in head-to-head studies.

RWD can also flag previously unrecognized safety concerns for comparator drugs. These signals can guide further investigation and support a fairer comparison when evaluating novel therapies.

Putting RWD in Practice

The integration of RWD into ACTs is no longer hypothetical; it is increasingly a regulatory and scientific reality.

Insights from RWD not only contextualize ACT findings but also contribute to clinical guideline development and shared decision-making between clinicians and patients.

The FDA and European Medicines Agency have both articulated frameworks for the use of RWD in regulatory decision-making. For example, RWD can serve as an external control when an RCT is infeasible. It can also provide supplemental analysis by strengthening and clarifying the safety and effectiveness information for underrepresented populations. 

Post-market, regulators can require sponsors to conduct RWD-based studies to fill gaps in comparator safety data.

Optimally, planning for the incorporation of RWD should be taken into account when the trial is designed and documented in the protocol. This represents a proactive approach to study design, which may negate the need for later research to prove to regulatory bodies that the limitation known to exist in the active control does not exist in the drug under submission.

De-identified real-world data (RWD), including active longitudinal records sourced from an array of healthcare settings and technologies, may be used to identify safety signals to monitor during the trial, as well as efficacy limitations among various ethnicities and populations.

The use of RWD ensures that treatment evaluations reflect the experiences of all populations, not just the trial-eligible few. It also helps reduce unnecessary trial enrollment when answers can be derived from existing data.

Advancing Research

ACTs remain a cornerstone of therapeutic evaluation, but they are only as strong as the underpinning data. For older drugs, especially those approved before the modern era of systematic safety monitoring, comparator data may be incomplete or misleading.

Real-world data offers a practical way to bridge this gap. By capturing safety and effectiveness across diverse, long-term, and unselected patient populations, RWD supplements trial evidence and ensures that comparisons are fair, contextualized, and clinically meaningful.

In the era of precision medicine and rapid therapeutic innovation, the thoughtful integration of RWD into ACT design and interpretation is not just a methodological enhancement—it is a necessity for advancing patient-centered care.

About Ashley Daigneau

Ashley Daigneau is head of clinical trials at Verana Health, where she oversees the strategy and execution of innovative clinical research solutions leveraging real-world data. Ashley has more than 15 years of experience supporting the development of real-world evidence strategies and overseeing clinical study execution.