Are Psychiatric Trials Over-Standardized — Costing Us Signal?

By Shayne Ladak, MD, MPH, CMD | Principal Investigator, IMA Clinical Research Albuquerque

Introduction: The Pattern We Keep Ignoring

If you work in CNS drug development, the pattern is familiar—almost predictable.

Promising compounds fail to separate from placebo. Effect sizes remain modest, even in well-executed trials. Phase II signals that once looked encouraging seem to dissolve by the time they reach Phase III. These are not isolated occurrences; they are recurring outcomes that have become embedded in the field.

At the site level, this pattern is not theoretical—it is lived in real time.

In one recent depression trial, a patient entered the study with clear anhedonia and profound functional withdrawal. Over the early weeks of treatment, meaningful clinical change was evident: increased engagement, a return to daily structure, and noticeable improvement in affect. Yet when measured through a standardized depression scale, the total score showed minimal movement. Persistent sleep disturbance and appetite changes—symptoms unrelated to the investigational drug’s mechanism—diluted the measurable impact.

In another study, two patients met identical inclusion criteria for major depressive disorder. On paper, they were equivalent. In reality, they were not. One exhibited melancholic features and psychomotor slowing, while the other presented with anxiety, insomnia, and somatic distress. Despite likely differences in underlying biology and treatment responsiveness, both were grouped together for randomization and endpoint analysis.

When these trials fail, the post-mortem is often predictable. We examine execution. We question endpoints. We revisit site performance. But beneath these surface-level explanations lies a more uncomfortable—and more consequential—truth: psychiatric trials are built on a level of standardization that the underlying biology does not support.

In our attempt to reduce noise, we may be systematically diluting signal.

The Foundation of Modern Psychiatric Trials

Modern psychiatric trial design rests on three core pillars: DSM-based diagnostic categories, standardized rating scales, and rigid inclusion and exclusion criteria. These tools were developed to solve a very real problem—namely, inconsistency in diagnosis and measurement. They brought structure to a field long defined by subjectivity and helped establish reproducibility and regulatory alignment.

However, in doing so, a critical assumption took hold: that consistency implies validity.

It does not.

Patients who meet criteria for the same psychiatric diagnosis may share remarkably little symptom overlap. Comorbidity is not the exception—it is the rule. Outcomes can vary widely depending on symptom clusters, rater interpretation, and even the construction of the scale being used.

Despite this, trial design continues to treat these populations as if they are homogeneous. This assumption is not merely conceptual—it is embedded structurally into how trials are designed and executed. And it has direct consequences for signal detection.

Are We Standardizing the Wrong Things?

Unlike oncology or infectious disease, psychiatric diagnoses are not discrete biological entities. They are syndromic constructs—clusters of symptoms rather than clearly defined mechanisms.

As Steven E. Hyman has noted, psychiatry has long struggled with the “reification” of diagnostic categories—the tendency to treat conceptual groupings as though they represent biologically distinct diseases.

This creates a fundamental mismatch within clinical trials. The intervention being studied may be biologically specific, targeting a particular pathway or mechanism, while the population being enrolled is diagnostically broad and heterogeneous.

The result is predictable: signal dilution.

When biologically distinct patients are grouped together, true responders are averaged with partial responders and non-responders. The sharper and more targeted the mechanism of the drug, the more this mismatch suppresses detectable effect. In this context, standardization does not clarify signal—it obscures it.

Why This Matters for Sponsors: From Concept to Cost

This issue is not abstract or philosophical. It is operational—and costly.

When heterogeneous populations are enrolled, treatment effects are blunted, making effective therapies appear less impactful than they truly are. At the same time, legacy rating scales often measure broad symptom aggregates rather than mechanism-specific domains. A drug that meaningfully improves anhedonia, for example, may fail to significantly shift a total depression score dominated by unrelated symptoms.

Compounding this problem is the amplification of placebo response. Subjective endpoints combined with high-touch trial environments can inflate placebo effects, further masking true drug-placebo separation.

Even when trials achieve statistical significance, they may fail clinically. The populations studied and the endpoints used often do not reflect real-world disease or meaningful patient outcomes. The result is a familiar disconnect: statistical success without clinical clarity.

The Hard Truth: We Are Studying Constructs, Not Diseases

At its core, the issue is this: we are not studying diseases—we are studying diagnostic constructs.

These constructs are imperfect proxies for underlying biology. Within any given diagnosis, heterogeneity is substantial. Comorbidity is pervasive. Diagnostic boundaries are fluid rather than fixed.

This is not solely a failure of trial design. However, continuing to treat these constructs as if they represent biologically coherent entities carries a cost—and that cost is missed signal.

What Forward-Thinking Sponsors Are Doing Differently

Despite these challenges, the field is already beginning to evolve in meaningful ways.

Some trials are moving toward transdiagnostic approaches, recruiting patients based on symptom domains such as anhedonia or agitation rather than strict DSM categories. Others are incorporating biomarker enrichment strategies—using genetic, neuroimaging, or digital markers to improve population precision, even if only incrementally.

Digital phenotyping is also gaining traction, allowing for continuous measurement of behavior, sleep, and activity. These tools capture dimensions of change that episodic rating scales often miss. At the same time, adaptive trial designs are enabling studies to evolve dynamically, refining populations and endpoints based on interim data rather than remaining static.

These approaches do not represent a rejection of rigor. Rather, they reflect a shift toward aligned rigor—designing trials that better match the biological and clinical realities they aim to study.

What Site-Level Physicians See—But Trials Often Miss

At the site level, the disconnect between protocol and patient reality is difficult to ignore.

Patients do not present as DSM archetypes. Symptoms fluctuate over time. Functional improvement and scale scores often diverge in meaningful ways.

Clinicians frequently observe early signs of improvement—in engagement, cognition, or behavior—well before these changes are reflected in standardized rating scales. Yet these observations are often dismissed as subjective or anecdotal.

In reality, they may represent early signal—signal that current measurement tools are simply not equipped to capture.

When trial design minimizes or excludes clinician insight in favor of strict standardization, it risks discarding precisely the nuance required to detect meaningful effects.

The Strategic Opportunity

For sponsors and CROs, this challenge represents not just a limitation, but a strategic opportunity.

Aligning trial design more closely with psychiatric reality can enable stronger early-phase signal detection, more reliable transitions from Phase II to Phase III, and clearer identification of responder populations. It also improves real-world differentiation—an increasingly critical factor in competitive therapeutic landscapes.

In CNS development, where many failures are non-pharmacologic in nature, design itself becomes strategy.

A More Practical Sponsor Takeaway: What Should Be Done Differently

The path forward is not to abandon standardization, but to apply it more intelligently.

Trial populations should be better aligned with mechanism, moving beyond broad DSM categories to incorporate symptom-domain or transdiagnostic entry criteria. Endpoints should be tailored to mechanism of action, supplementing total scores with targeted subscales or digital markers that capture meaningful change.

Rather than averaging across heterogeneous populations, trials should stratify—pre-specifying subgroups based on phenotype or biomarker signals to better detect responder populations. Clinician insight should be integrated into trial design through structured global impressions and careful attention to early clinical change.

Adaptive designs should be embraced, allowing trials to refine inclusion criteria and endpoints as data emerges. And wherever possible, artificial homogeneity should be reduced by reconsidering the exclusion of common comorbidities and designing for populations that more closely reflect real-world patients.

The goal is not less rigor. It is better alignment between biology, measurement, and population.

A Practical Lens: Are We Reducing Noise—or Eliminating Reality?

All clinical trials aim to reduce variability. This is both necessary and appropriate. But not all variability is noise. Some variability is intrinsic to the disease itself.

When protocols over-constrain populations and rely on blunt measurement tools, they may produce datasets that appear cleaner—but are ultimately less valid. The critical question is not whether a trial is standardized, but whether that standardization is aligned with the illness being studied—or whether it simplifies the condition to the point that signal disappears.

Final Thought
The Future of CNS Trials Will Be Smarter, Not Simpler

Standardization has played a foundational role in modern clinical trials, bringing discipline, comparability, and regulatory credibility. But in psychiatry, it has limits.

The next generation of CNS trials will not be defined by greater simplicity, but by more precise, better-aligned trial design. These trials will be built around heterogeneity rather than in spite of it. They will focus on mechanisms rather than diagnoses, measure meaningful change rather than familiar change, and integrate clinical judgment alongside structured assessment.

Psychiatric illness is complex, heterogeneous, and resistant to simplification. Our trial designs must reflect that reality.

Because the ultimate goal is not cleaner data.

It is detectable signal.

Continuing the Conversation at ASCP 2026

IMA Clinical Research will be at the American Society of Clinical Psychopharmacology (ASCP) Annual Meeting in Miami, FL, May 26–29.

To set up a meeting at ASCP, please contact: [email protected]

References

Allsopp, K., Read, J., Corcoran, R. and Kinderman, P. (2019) ‘Heterogeneity in psychiatric diagnostic classification’, Psychiatry Research, 279, pp. 15–22.

Fried, E.I. and Nesse, R.M. (2015) ‘Depression sum-scores don’t add up: why analyzing specific depression symptoms is essential’, BMC Medicine, 13(1), pp. 1–11.

Hengartner, M.P. (2017) ‘Why psychiatric research must abandon traditional diagnostic classification and adopt a fully dimensional scope’, Frontiers in Psychiatry, 8, p. 101.

Hyman, S.E. (2010) ‘The diagnosis of mental disorders: the problem of reification’, Annual Review of Clinical Psychology, 6, pp. 155–179.

Newson, J.J., Hunter, D. and Thiagarajan, T.C. (2021) ‘The heterogeneity of mental health disorders: a network perspective’, Frontiers in Psychiatry, 12.