Verify AI-Enabled
Statistical Analysis Platform

Accelerate TLF validation processes, streamline workflows, and enhance visibility

The Verify Essentials

Speed review cycles with a purpose-built control tower for centralized TLF review and QC tracking.

Optimized collaboration and review

Unify all TLF review roles within Verify to ensure that everyone has access to the most up-to-date information – avoiding  delays caused by scattered emails, offline spreadsheets, and marked-up PDFs. Save 2 hours of manual effort daily by working within a synchronized platform where you can comment directly on digitized TLFs, adjudicate issues, assign responsibilities, update priorities, and share files.

Centralized end-to-end QC management

Gain real-time visibility and oversight with automated QC progress and task tracking, so that you can quickly address bottlenecks, accelerate decision making, and monitor overall delivery readiness.

Inspection-ready audit trail

Every action is automatically captured in an immutable log to support traceability, accountability, and regulatory compliance.

Verify Validate

Accelerate early error detection and upstream remediation with an extensive library of AI-enabled validation checks.

Comprehensive AI-enabled TLF analysis

Verify’s AI-enabled parsing technology converts thousands of static TLFs into a machine-readable database, enabling standardized analysis of your delivery sets without study-specific code or configurations.

Early-stage high quality outputs

Harness AI power to quickly identify errors and minimize visual review, and instead focus your expertise on high-value aspects of clinical trials, such as primary efficacy endpoints and complex algorithms and analyses.

Verify AI-enabled validation checks

  • Format Checks validate formats, titles, footnotes, and TLF structural characteristics
  • Reference Checks validate consistency of TLFs against reference documentation such as mock shells and LoT/ToC
  • Within-Table Checks validate arithmetic and hierarchies within tables
  • Cross-Table Checks validate consistency across table sets

Verify Generate

Leverage AI and human expertise to build a structured metadata store, powering TLF validation, data transformation, and metadata reuse. Preserve and utilize data lineage while creating multiple parallel streams of value from a single resource.

AI-powered table generation for validation

Verify automates the extraction of results metadata by reverse engineering your tables and linking table entities to corresponding ADaM columns and values. Verify uses this metadata alignment to generate tables, enabling accurate Table vs ADaM Checks.

Seamlessly coordinated AI and human expertise

Use Verify’s Metadata Curator to review and cultivate AI-identified metadata alignment, providing key human-in-the-loop (HITL) expert feedback and oversight to refine your structured metadata store.

Unlock the benefits of a high-quality structured metadata store

Your curated metadata is retained within Verify, preserving data lineage and enabling a unified, traceable picture of statistical summary data and its metadata antecedents. Your structured metadata store enables high-quality AI input, providing the basis for advanced AI-powered table generation, metadata management and reuse, and data transformation to align with standards.

Resources

All Resources

Case Study: How Phastar Uses Verify to Accelerate Clinical Data Review

Accelerating Clinical Data Review: Addressing Fragmentation, Improving Collaboration, and Reducing Review Cycle Times by 35% Download PDF: Phastar-Beaconcure Case Study Industry Challenge: Disconnected Review Workflows, Lack of Automation, and Lengthy Data Review Cycles Clinical data analysis review remains a critical, yet often fragmented, element of the clinical trial process. Many organizations still rely on manual […]
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Beaconcure Podcast: Clinical Trial Innovation – Spotlight on Quality, Review, and Collaboration

In this video podcast, Névine Zariffa, leading voice in biometrics and data science, and Beaconcure Co-Founder and COO Ilan Carmeli, discuss the future of technology in improving clinical trial efficiency and quality during statistical analysis review. Névine and Ilan discuss some of the challenges reviewers currently face using traditional review processes, and Ilan demonstrates new […]
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Blog

The Future of Statistical Computing: Environment Platforms

While an “ideal” Statistical Computing Environment (SCE) is highly sought for in the pharmaceutical industry, one is yet to be developed. To encourage tech business leaders to develop such a platform, experts from top global pharma companies have compiled their requirements for the ideal SCE. These requirements were published in December 2021 in a white […]
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BENEFITS OF AUTOMATING STATISTICAL ANALYSIS Blog

Benefits of Automating Statistical Analysis for Clinical Studies

The Current Status Statistical analysis output validation takes time and effort but does not guarantee a high-quality deliverable. Errors are still likely to go uncorrected. When submitting a study to the regulator, the expectation is that the output validation tasks -which are lengthy and expensive timewise –  will be done with impeccable accuracy.  The validation […]
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