What Is Adjudication in Clinical Trials?

Adjudication in clinical trials is a quality-control process in which an independent panel of experts reviews clinical events reported during a study to confirm whether they genuinely meet the trial’s predefined endpoint criteria. Instead of relying solely on the judgment of individual site investigators scattered across dozens or hundreds of hospitals, a central committee applies a uniform standard to every suspected event while blinded to which treatment each patient received.¹ The practice is most common in large cardiovascular, stroke, and device trials, where even small discrepancies in how a heart attack or death gets classified could swing the overall results.

How the Process Works in Practice

When a patient in a clinical trial experiences a medical event that could count as an endpoint, such as a heart attack, stroke, or death, the local site investigator files a report. That report, along with medical records, lab results, imaging, and discharge summaries, gets forwarded to a central group variously called a clinical events committee (CEC), endpoint adjudication committee (EAC), or clinical endpoint committee. Two or more members of the committee independently review the documentation and decide whether the event satisfies the trial’s endpoint definitions.¹

If the reviewers disagree, the case typically goes to a third reviewer or a full committee discussion until a consensus is reached. The entire process runs on a pre-written charter that spells out the endpoint definitions, rules for case routing, timelines, and how disagreements get resolved. This charter is finalized before the trial begins collecting data, specifically to prevent anyone from adjusting the criteria after seeing partial results.²

Why Blinding and Independence Are Non-Negotiable

The committee’s credibility rests on two things: blinding and independence. Members are kept blind to whether the patient whose case they are reviewing was in the treatment group or the control group. Because they cannot tell which arm a patient belongs to, any errors they make in classifying events should be random rather than systematically favoring one treatment over another. This protects against detection bias, a form of systematic error that can quietly distort a trial’s conclusion.³

Independence is defined strictly. An independent CEC member cannot be primarily involved in designing, funding, sponsoring, organizing, conducting, analyzing, or regulating the trial they serve on. They also need to be free of conflicts of interest with the steering committee, sponsor, manufacturer, and participating clinical sites.⁴ This separation exists because even well-intentioned researchers can unconsciously lean toward classifying events in ways that favor the treatment they helped develop or the sponsor who funds their work.

Having a small, central team of specialists review every event also reduces a different kind of noise. When hundreds of site investigators each apply their own clinical judgment, some will be stricter and others more lenient about what counts as a qualifying event. A central committee applying one consistent standard smooths out that variability, which in practice means reducing random error and potentially boosting the trial’s ability to detect a real treatment effect.³

How Often Adjudicators Disagree with Site Investigators

If adjudicators and site investigators always agreed, the process would be expensive busywork. They don’t always agree, but the degree of disagreement varies by the type of event being assessed.

In a secondary analysis of the PARITY trial, which studied infection outcomes in orthopedic surgery, the central adjudication committee and site investigators showed substantial agreement on the primary outcome of surgical site infection diagnosis. For finer distinctions, like categorizing whether an infection was superficial, deep, or organ-space, agreement was only moderate. For less clear-cut outcomes like antibiotic-related complications, agreement dropped further to what statisticians would classify as fair.⁵

In the SHIFT heart failure study, investigators identified over 7,500 prespecified endpoints. The central committee confirmed about 98% of cardiovascular deaths but only about 84% of hospitalizations for worsening heart failure.⁶ The pattern makes intuitive sense: death is hard to misclassify, while deciding whether a hospitalization was driven by worsening heart failure versus another cause requires more subjective judgment.

In the PROGRESS stroke trial, investigators initially reported 992 strokes, and the adjudication committee retained 90% of those diagnoses.⁷ Again, the committee served as a filter, discarding about one in ten events that didn’t meet the strict endpoint definition on closer review.

Does Adjudication Actually Change a Trial’s Bottom Line?

This is where the evidence gets surprisingly consistent, and somewhat humbling for proponents of the process. Across multiple large trials, the treatment effect estimates based on adjudicated outcomes and investigator-reported outcomes have been remarkably similar.

In the SHIFT heart failure trial, the hazard ratio for the primary composite endpoint was 0.83 based on investigator reports and 0.82 based on the committee’s adjudicated data, with comparable results for individual components like cardiovascular death and hospitalization.⁶ In the PROGRESS stroke trial, hazard ratios for the effect of treatment on stroke were 0.74 using investigator diagnoses and 0.72 using adjudicated diagnoses, with no statistically meaningful difference between the two for any stroke subtype, for myocardial infarction, or for the main causes of death.⁷

The ADVANCE trial told a similar story. The adjudication committee discarded a small number of events from each treatment arm, which slightly widened the confidence intervals around the treatment effect. But the point estimates themselves were essentially unchanged. The authors concluded that the primary impact of adjudication in their trial was to slightly reduce statistical power by removing events, without any evidence that the unadjudicated outcomes had been biased.⁸

These findings fuel a real debate in the clinical trials community. If adjudication rarely changes the overall conclusion, is it worth the cost and effort? The counterargument is that adjudication serves as insurance: you cannot know in advance whether a particular trial will be one where investigator reporting happens to be unbiased. The process exists to catch the cases where it isn’t. And the fact that most trials come out looking similar with or without it could be read as a sign that the broader system of endpoint definition and investigator training is working well enough to keep site-level reporting honest.

The Cost of Getting It Right

Adjudication is not cheap. A review of nine cardiovascular trials found that the process cost more than $100,000 in roughly a third of them, with an average cost of about $2,500 per event that the committee actually reclassified from the investigator’s original call.⁹ That per-event figure puts the trade-off in stark terms. In a trial with thousands of endpoints, even a low rate of reclassification can generate significant costs. In a smaller trial, the math becomes harder to justify.

Beyond dollar figures, adjudication demands a substantial infrastructure of research personnel to collect and organize source documents from clinical sites, redact information that could unblind reviewers, route cases to committee members, track disagreements, and manage timelines. The process also requires that qualified specialists be available on an ongoing basis, sometimes for years in long-running trials. In some therapeutic areas, the pool of experts who are both qualified and free of conflicts is small enough that scheduling becomes its own bottleneck.

These practical realities help explain why there are different opinions about when central adjudication is worth deploying versus when simpler approaches, like standardized site-based assessment with thorough training, might suffice.¹

Adjudication Beyond Cardiology

Most of the published evidence about adjudication comes from cardiology and stroke research, where the practice has the longest track record. But the concept applies wherever endpoints require judgment calls. In orthopedics, for example, outcomes like fracture healing or surgical site infection involve subjective assessment that can vary widely from one surgeon to another. Adjudication has been described as providing more reliable outcome assessment in these settings, though it has historically been less commonly reported in the orthopedic literature than in cardiovascular trials.¹⁰

The challenge grows when a trial’s primary endpoint is something like “time to fracture union,” which depends on imaging interpretation. Two surgeons looking at the same X-ray can disagree about whether a fracture has healed. A central committee applying standardized imaging criteria can impose consistency, but the process requires even more infrastructure because imaging files are large and must be anonymized and transmitted securely. Device trials add another layer: the committee may need to determine not just whether an event occurred but whether the device itself contributed to it, which requires specialized engineering and clinical expertise.

How Technology Is Changing the Workflow

Traditional adjudication involved mountains of paper: printed medical records, photocopied lab reports, and physical binders shipped to reviewers. The shift to electronic systems has changed the logistics considerably. Web-based endpoint adjudication platforms allow committee members to review case materials online, submit their decisions through structured electronic forms, and have those decisions immediately incorporated into the trial database. This automation reduces errors from manual data handling, speeds up the process, and makes adjudicated data available in time for interim analyses.¹¹

A more recent development is hybrid systems that use algorithms to pre-screen events before a human reviewer sees them. In one such system tested across two cardiovascular trials, the automated component cut the time required for human review roughly in half, from about 1,300 hours to about 700 hours in one trial and from roughly 390 hours to about 200 hours in the other.¹² The algorithms handle straightforward cases, flagging only ambiguous ones for expert review. This approach preserves the human judgment that adjudication is built on while acknowledging that a large fraction of events are clear-cut and don’t need a specialist to spend twenty minutes reaching the obvious conclusion.

Artificial Intelligence as a Potential Adjudicator

Researchers are now testing whether AI can go further than pre-screening and actually make adjudication decisions. A system called Auto-MACE, trained on data from a heart failure trial, was able to provide a confident adjudication for about 69% of deaths, 46% of potential heart attacks, and 81% of potential strokes. Among the events where the system felt confident, it agreed with the human committee’s decisions 97% of the time for deaths, 89% for heart attacks, and 88% for strokes. The treatment effect estimate produced using Auto-MACE’s adjudications was nearly identical to the one produced by the human committee.¹³

A separate effort called ADAPT-CEC took a different approach, training an AI algorithm on adjudication data from one cardiovascular trial and then adapting it to work on a different trial with different endpoint definitions, using only about 20 example cases per endpoint from the new trial for calibration.¹⁴ This adaptability matters because endpoint definitions are not universal. What counts as a myocardial infarction in one trial may use slightly different biomarker thresholds or timing criteria than another. An AI system locked to one set of definitions has limited practical value.

Earlier work using machine learning showed classification performance above 97% on validation data, with the site investigator’s own decision, patient sex, and clinical notes about symptoms like edema and chest pain ranking among the top features the algorithm relied on.¹⁵ The researchers suggested that machine learning could augment or even replace clinician adjudication in cardiovascular outcome trials, though the field is far from that point in practice.

The appeal is obvious: if an AI system can replicate human committee decisions with high accuracy, it could slash costs and turnaround times while keeping the standardization that adjudication is designed to provide. The concern is equally obvious. Adjudication matters most in ambiguous cases, which are precisely the cases where AI performance drops. Auto-MACE’s agreement rate fell to 76% for potential heart attacks when all events were included, not just the ones the system flagged as confident.¹³ For a process whose entire purpose is to serve as a trustworthy final arbiter, that gap is not trivial.

Pragmatic Trials and Real-World Data

Adjudication was designed for tightly controlled efficacy trials, but a growing number of studies are pragmatic trials that rely on real-world clinical and administrative data rather than carefully curated case report forms. This creates a different set of problems. In one pragmatic trial conducted within the Veterans Affairs healthcare system, researchers found that looking only at VA records identified a cardiovascular event rate of 3.5%, but pulling in Medicare data from outside the VA system revealed an additional set of events that pushed the rate to 5.7%.¹⁶ If a substantial fraction of events happen at facilities outside the trial’s data network, no adjudication committee can review what it never sees.

Cause-of-death data illustrates a related wrinkle. In that same trial, national death index data did not increase the total number of deaths captured beyond what VA records already showed, but it did improve the accuracy of determining cause of death.¹⁶ For a trial whose primary endpoint is cardiovascular death specifically, knowing that someone died is not enough; you need to know why. Adjudication of cause of death in pragmatic settings often depends on data sources with built-in delays, sometimes a year or more, which complicates timely analysis.

These challenges are pushing researchers to think about adjudication differently in pragmatic contexts. Rather than a central committee reviewing individual case files, some pragmatic trials are exploring algorithmic approaches that flag likely events from electronic health records and administrative claims, with human review reserved for borderline cases. The right balance between thoroughness and feasibility likely depends on the specific endpoint, the data sources available, and how much misclassification a trial can tolerate before its conclusions become unreliable.

1
PubMed Central. When do we need clinical endpoint adjudication in clinical trials?
2
American Heart Journal. Use of endpoint adjudication to improve the quality and validity of endpoint assessment for medical device development and post marketing evaluation
3
PubMed Central. Should we adjudicate outcomes in stroke trials? A systematic review
4
PubMed. Independence of clinical events committees: A consensus statement from clinical research organizations
5
PubMed. Central Adjudication Committee and Clinical Site Investigator Agreement on Outcomes in the PARITY Trial
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PubMed. Comparison of Outcome Adjudication by Investigators and by a Central End Point Committee in Heart Failure Trials
7
PubMed. Effects of the end point adjudication process on the results of the Perindopril Protection Against Recurrent Stroke Study (PROGRESS)
8
PubMed Central. Effects of the Endpoint Adjudication Process on the Results of a Randomised Controlled Trial: The ADVANCE Trial
9
European Heart Journal. Endpoint adjudication in cardiovascular clinical trials
10
The Journal of Bone and Joint Surgery. Adjudicating Outcomes: Fundamentals
11
PubMed Central. A web-based endpoint adjudication system (WebEAS) for interim analyses in clinical trials
12
PubMed. A hybrid automated event adjudication system for clinical trials
13
PubMed Central. Using Artificial Intelligence to Adjudicate Major Adverse Cardiovascular Events in Clinical Trials
14
PubMed. Adaptive AI for Cardiovascular Event Adjudication
15
Circulation. Abstract 9330: Machine Learning Can Enable the Automation of Clinical Endpoint Adjudication
16
Journal of Biomedical Informatics. Strategies for secondary use of real-world clinical and administrative data for outcome ascertainment in pragmatic clinical trials