Detect, Quantify, and Correct Clinical Trial Bias
The first end-to-end platform for clinical trial bias management. From demographic underrepresentation to methodological flaws, detect issues early and correct them before regulatory submission.
Bias Threatens Trial Validity
From demographic underrepresentation to methodological flaws, bias compromises scientific validity, distorts treatment effects, and triggers regulatory rejection. Most issues remain invisible until submission—when it's too late.
of clinical trials contain systematic bias
cost per protocol modification
potential cost reduction with bias correction
Types of Bias We Detect
Demographic Bias
Underrepresentation of women, elderly, children, and ethnic minorities in trial populations
Selection Bias
Flawed randomization or allocation concealment affecting group assignment
Performance Bias
Inadequate blinding causing behavioral differences between groups
Detection Bias
Outcome measurement influenced by knowledge of treatment assignment
Attrition Bias
Non-random patient withdrawals skewing study results
Reporting Bias
Selective reporting of statistically significant outcomes only
Business and Clinical Impact
Financial Risk
Protocol modifications due to bias cost $17.3M (Phase I) to $200M (Phase III). Studies with high risk of bias show exaggerated treatment effects that fail to replicate, wasting development investment.
Regulatory Scrutiny
FDA and EMA increasingly require documented evidence of representative enrollment and robust methodology. Trials with inadequate randomization, poor blinding, or unrepresentative populations face rejection.
The First End-to-End Bias Management Platform
Clinequal integrates detection, quantification, explanation, and correction into a single workflow. Unlike EDC systems or generic data cleaning tools, we address systematic bias at its source with regulatory-ready outputs.
Bias Scanner
Automated detection of demographic imbalances and methodological flaws: population underrepresentation, randomization issues, blinding gaps, attrition patterns, and selective reporting.
Quantification & Explainability
Fairness metrics quantify bias magnitude and statistical significance. Explainable AI (XAI) traces root causes to specific enrollment sites, inclusion criteria, or protocol design decisions.
Correction & Simulation
Evidence-based correction strategies: statistical weighting, subsampling, targeted enrollment recommendations. What-if simulations model outcome changes before protocol amendments.
Regulatory Compliance
Audit-ready reports for EMA and FDA submission, aligned with Cochrane standards. Full audit trails, GDPR/MDR compliance, ISO 27001 security. Documentation in 24+ languages.
See It in Action
Explore bias detection on sample clinical trial datasets. No signup required.
2 sample trials • ~2 min walkthrough
From Dataset Upload to Regulatory Submission
Six integrated modules transform biased trial data into defensible, representative datasets with full documentation for submission.
Detect Bias
Automated scanning for demographic imbalances (gender, age, ethnicity) and methodological issues: randomization flaws, blinding gaps, attrition patterns, and reporting inconsistencies.
Quantify Impact
Fairness metrics measure the magnitude and statistical significance of each bias type. Visualizations show how bias may inflate or deflate treatment effect estimates.
Explain Root Causes
Explainable AI traces bias to specific protocol decisions: allocation methods, blinding procedures, outcome definitions, dropout patterns, or selective reporting practices.
Correct Datasets
Evidence-based correction strategies: statistical weighting, sensitivity analyses, imputation methods for missing data, and recommendations for protocol amendments.
Simulate Scenarios
What-if modeling: 'How do results change if we adjust for attrition bias?' or 'What if allocation concealment was improved?' Test corrections before implementation.
Generate Reports
Regulatory-ready documentation for EMA/FDA submission, aligned with Cochrane methodology. Full audit trails, 24+ languages, GDPR and MDR compliance built in.
Recognition & Achievements
Selected from 495 applicants for Fit4Start Luxembourg. Winner of the US-Italy Hackathon and Startcup Campania Social Impact Prize.
Fit4Start Finalist
Luxembourg Accelerator Program
Luxembourg
Academic Collaborations
Leadership Team
Expertise spanning AI/ML, bioinformatics, healthcare data science, software engineering, and regulatory compliance. Advised by scientists in the top 2% most cited globally.
Reduce Regulatory Risk with Representative Datasets
Schedule a consultation to see how Clinequal addresses bias in your trial data before submission.
Email us at info@clinequal.com
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