Impact of Data Concerns in Neurological Clinical Trials: Why Quality Matters

Part 1 of our 3-part series on blinded data analytics in CNS Clinical Trials 

Data quality can make or break a study's success in the complex landscape of clinical trials, particularly those focusing on neurological conditions.  

While researchers design trials with the utmost care and statistical rigor, data concerns arise frequently and can significantly impact outcomes.  

Understanding these concerns and implementing effective strategies to address them is crucial for ensuring trial integrity and validity. 

How Common Are Data Concerns in Neurological Trials? 

Data from our analysis reveals that data concerns are surprisingly prevalent in neurological clinical trials.  

Our research shows that in Alzheimer's disease trials, around 46% of data points may raise quality concerns. Similarly, Parkinson's disease trials show approximately 25% data concern rates, with other indications hovering around 24.5%. 

These aren't just minor statistical blips—they represent significant clinical challenges that can affect trial outcomes. 

Types of Data Concerns in Neurological Trials 

Different neurological conditions present unique data challenges: 

Alzheimer's Disease Data Concerns 

• Inclusionary criteria violations
• Scoring errors in cognitive assessments 
• Errors in the administration of cognitive assessments 
• Issues with assessment duration
• Excessive variability in measurements

 Parkinson's Disease Data Concerns 

• Discordance between different rating scales
• Excessive or insufficient variability in assessments
• Inappropriate assessment duration
• Inclusionary criteria issues
• Problems with lateral predominance tracking   

The Ripple Effect: How Data Concerns Impact Signal Detection 

Perhaps most concerning is how these data quality issues can affect a trial's ability to detect true treatment effects.  

Our simulation studies demonstrate that introducing even modest errors can dramatically alter outcomes: 

• With minimal error (SD = 5, 5% of data affected), study outcomes remained relatively intact, with only increased variance in placebo and drug responses 
• With a more significant error (SD = 20, 20% of data affected), we observed:
  • 34.2% of simulated trials failed to differentiate drug from placebo
  • Substantial variation in placebo and drug responses, often reaching unrealistic or implausible values. Such data inaccuracies risk artificially elevating placebo responses (compromising drug-placebo differentiation) or overstating drug efficacy, either of which can distort critical development milestones and regulatory evaluations. In poorly powered trials, these effects become even more pronounced. 

Issue Clustering:  A Basis for Targeted Action 

Concentrated data quality problems at a few sites can lead to unreliable data that may distort the trial’s conclusions about safety and efficacy. This can result from intentional fraud, misconduct, or unintentional errors such as biases or noise in data recording and reporting.  

For example, our analysis of chair rise assessment data in one trial revealed that approximately 11% of all data and 15% of randomized subjects showed concerning patterns of implausible values. Further investigation demonstrated that these concerns weren't randomly distributed but clustered at a small number of sites. 

In addition, biases introduced at specific sites (e.g., assessment bias as in our case) can skew the overall study results. Noise and errors dilute the treatment effect signal, potentially obscuring true differences between treatment arms and leading to false negative or false positive outcomes. 

Fraud or significant noncompliance at a few sites can lead to regulatory actions, study invalidation, or rejection of data for product approval. This undermines public trust in clinical research and may require costly rework or trial repetition. 

However, such concentration of issues facilitates earlier identification through centralized data monitoring techniques, statistical anomaly detection, or triggered on-site visits. Early intervention can prevent the spread of data errors and preserve data integrity. 

The Solution: PureSignal Analytics 

Given these challenges, implementing robust blinded data analytics methodologies provides a powerful solution. Effective approaches include: 

1. Identify concerns early: Catch data issues before they compound
2. Preserve study blinding: Address data quality without compromising trial integrity
3. Reduce variability: Minimize noise that might obscure treatment effects
4. Improve signal detection: Enhance the ability to identify true treatment effects 

To maximize effectiveness, implementations should be: 

• Immediate: Providing real-time feedback when issues arise
• Clinically informed: Based on deep understanding of the condition being studied 
• Actionable: Offering specific remediation steps for identified issues 

Conclusion 

Data concerns are not just an academic concern—they have real-world implications for patients waiting for effective treatments. As our simulation studies show, poor data quality can lead to both false positives (approving ineffective treatments) and false negatives (missing effective treatments). 

Robust blinded data analytics methodologies represent an important component of the comprehensive solutions available to sponsors for optimizing trial outcomes. When integrated with other quality assurance measures, these analytics can help ensure accurate results, minimize unnecessary exposure of subjects to ineffective treatments, and contribute to more efficient development processes that reduce costs while accelerating the delivery of effective neurological treatments to patients in need. 

For more information on how to implement effective blinded data analytics in your neurological clinical trials, contact our team of clinical data specialists. 

About the Author

Dr. Alan Kott is the Practice Leader for Data Analytics at Signant Health, with both academic and industry experience in clinical trials. He has led the development of Signant’s Data Analytics Program, overseeing data analytics in over 200 clinical trials across multiple indications. Prior to joining Signant, Dr. Kott was an Assistant Professor at Charles University and a house officer in psychiatry at General Teaching Hospital in Prague. He holds a Medicinae Universae Doctor (MUDr.) from Charles University.

Dr. Alan Kott's presentation, "Application of BDA Methodologies in Clinical Trials Focus on Neurology," was supported by Petra Reksoprodjo (Director of Clinical Program & Performance, Operations) and Chris Murphy (Associate Director Clinical Service, Operations), and presented at ISCTM in Washington, D.C.