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Reasons CNS Trials Fail to Separate Drug from Placebo

Signant Health
Jun 19, 2026
eBook
Participant dropout strategies in anti-obesity medication clinical trials

CNS trials fail at higher rates than most other therapeutic areas, and the reasons are well documented: subjective endpoints vulnerable to scoring variability, placebo response that can exceed the response rates of previously approved active medications, and patient selection complicated by diagnostic uncertainty and baseline score inflation.  

This eBook brings together nine clinical scientists and data analytics experts to examine three specific, actionable areas where sponsors can intervene before the first participant is enrolled. 

Key Points 

  • Analysis of 10,203 Mini-Mental State Examination assessments across two large multinational Phase 3 Alzheimer's disease trials found 26.8% flagged for administration errors and 27.0% flagged for scoring errors, demonstrating that rater error is a systematic, measurable problem rather than an isolated one.
  • A Signant Health internal study pooling 47,238 ADAS-Cog assessments across 14 global dementia trials found administration and scoring errors in 19.6% of visits, with Number Cancellation (23.38%) and Constructional Praxis (20.48%) generating the highest error rates.
  • Independent Psychiatric Eligibility Reviews reduce diagnostic noise by providing centralized, standardized adjudication of screening data, controlling for cognitive bias at the site level, and adding a patient safety check that site investigators operating under recruitment pressure cannot reliably provide alone.
  • Evidence-based site selection using historical performance data analytics, powered by PureSignal Analytics, identifies sites and raters with demonstrated data quality track records before enrolment begins, shifting quality management from reactive remediation to proactive prevention.
  • Approximately one-third of adults with a confirmed psychiatric diagnosis have a comorbid psychiatric disorder, making differential diagnostic accuracy in CNS trial screening a direct determinant of study population homogeneity and signal detection probability.
  • The full eBook contains the complete MMSE and ADAS-Cog error taxonomy, the DSM-5-TR Differential Diagnosis Model framework applied to independent eligibility reviews, and the PureSignal Analytics site selection methodology, none of which are reproduced in full here

Why It Matters Now: Rater Error and Patient Selection Are Modifiable, Not Inevitable 
 
For a participant with Alzheimer's disease completing a cognitive assessment, the quality of the data generated depends entirely on how accurately the rater administers and scores the instrument in front of them. When a rater uses the wrong version of the ADAS-Cog scoring manual, provides an unpermitted prompt during the Serial 7s subtest, or applies inconsistent scoring criteria for Orientation to Place, the data that enters the trial record does not reflect the participant's true cognitive state. It reflects the error. Across thousands of assessments and hundreds of sites, those errors accumulate into noise that can make a genuinely effective treatment appear inactive. 
 
The same principle applies at screening. When a participant is enrolled with a misdiagnosis, whether Major Depressive Disorder in a patient with Bipolar II, or schizophrenia in a patient whose psychotic features were contextual rather than primary, the study population becomes more heterogeneous, statistical power decreases, and the drug's efficacy signal becomes harder to detect. 
 
Clinical development success rates in psychiatry and neurology remain modest compared with most other therapeutic areas, as documented in the BIO, QLS Advisors, and Informa analysis of 2011 to 2020 approval data. The interventions described in this eBook directly address the modifiable contributors to that gap. For sponsors designing CNS programs today, the evidence base for acting on these variables at the protocol stage rather than after data lock is clear and growing. 
 
What the eBook Contains 
 
The guide addresses adjacent problems that together determine whether a CNS trial is positioned to detect the signal it is designed to find. 
 
The first chapter, by Martina Micaletto, Alan Kott, and Petra Reksoprodjo, describes how PureSignal Analytics transforms site selection from a relationship-based exercise into an evidence-based one, using historical performance data across quality metrics, including assessment consistency, protocol compliance, and data completion rates. The system generates ranked site lists based on customizable quality criteria and, for sponsors with predetermined sites of interest, provides verification reviews that inform training and monitoring strategy before enrolment begins. 
 
The second chapter, by Juliet Brown and Rachel Berman, provides the most detailed publicly available framework for Independent Psychiatric Eligibility Reviews in CNS trials. It covers the specific conditions that make independent review necessary, the DSM-5-TR differential diagnosis model applied to eligibility decision-making, and the operational components that determine whether a review program adds genuine diagnostic confidence or functions as a procedural checkbox. 
 
The third chapter, by Marcela Roy, Sayaka Machizawa, Marta Pereira, and David Miller, draws on central review data from two multinational Phase 3 Alzheimer's disease trials and 14 global dementia trials to build a taxonomy of the specific MMSE and ADAS-Cog errors that most frequently compromise data quality. The findings are translated directly into rater training and central reviewer calibration recommendations grounded in observed error patterns rather than theoretical instruction. 
 
This is the first volume in an ongoing series. Future volumes will address advanced data analytics, innovative trial designs, central ratings and reviews, and indication-specific signal detection strategies across the CNS spectrum. 
 
 

 "The common thread across all interventions is the principle of proactive, prevention-focused strategies that establish robust foundations for signal detection rather than attempting to remediate issues after they arise." - David G. Daniel, MD, Executive Advisor, Signant Health; Conversations in CNS, Volume One  

How common are rater errors in MMSE and ADAS-Cog assessments in Alzheimer's trials?

Analysis of 10,203 MMSE assessments across two multinational Phase 3 Alzheimer's disease trials found 26.8% flagged for administration errors and 27.0% for scoring errors. A separate analysis of 47,238 ADAS-Cog assessments across 14 global dementia trials found errors in 19.6% of visits. The most common ADAS-Cog error items were Number Cancellation at 23.38% and Constructional Praxis at 20.48%.

When are Independent Psychiatric Eligibility Reviews needed in CNS trials?

Independent reviews are most critical in trials involving complex or at-risk diagnoses such as Borderline Personality Disorder, indications with heterogeneous symptom presentations such as dementia, and diagnoses commonly presenting with comorbidities that complicate differential diagnosis. They are also indicated when inclusion and exclusion criteria allow investigator subjectivity, and in pivotal trials where diagnostic confidence directly determines study population integrity and participant safety.

How does evidence-based site selection improve CNS trial outcomes?

Selecting sites based on historical data quality metrics, including assessment consistency, protocol compliance rates, and data completion, identifies sites with proven track records of generating reliable data before enrolment begins. PureSignal Analytics analyzes anonymized historical performance data across multiple dimensions to generate ranked site lists. This approach shifts quality management from reactive post-enrolment remediation to proactive prevention, with measurable impact on data integrity from the first participant enrolled.

 
 

AUTHOR BIO 

Name: David G. Daniel
Title and Credentials: MD, Executive Advisor at Signant Health
Bio: This volume is edited and introduced by David G. Daniel, MD, Executive Advisor at Signant Health, with over 30 years of experience in psychiatric clinical trials, extensive publications, and patents for treatments in epilepsy, anxiety, and psychotic disorders.  

Chapter contributors are Martina Micaletto, MSc, BSc, Manager Clinical Program and Performance, Digital Health Sciences; Alan Kott, MUDr, Practice Leader, Data Analytics; Petra Reksoprodjo, MUDr, Director, Clinical Program and Performance; Juliet Brown, PhD, Director, Endpoint Reliability; Rachel Berman, PhD, Associate Director, Digital Health Sciences; Marcela Roy, MA, Executive Director, Clinical Science and Medicine; Sayaka Machizawa, PsyD, Associate Director, Clinical Science; Marta Pereira, PhD, Clinical Scientist; and David Miller, MD, MA, Clinical Vice President. 

 

Designing site selection, eligibility review, or rater training strategy for an upcoming CNS program? Speak to the Signant Health CNS team about evidence-based approaches to signal detection across Alzheimer's disease, psychiatry, and neurology trials. 

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