AI LENS ACADEMY

Upskill your clinical analytics teams for modern clinical trial analysis

Cohort-based programs shaped by AI LENS experts help biometrics professionals blend statistical programming, metadata governance, and advanced clinical trial insight.

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Purpose-built learning paths translate our clinical trial analysis playbooks into guided coursework, live case reviews, and mentored projects that deliver immediate study impact.

10-week cohortDuration
50+ on-demand modules · 16 live sessionsLearning Format
4 expert-led Q&A intensivesExpert Access

Featured Certification

Deep-dive into the clinical trial analysis foundations behind AI LENS.

Cohort now enrolling

R for Clinical Trials: Master Clinical Data Analysis

Guided by clinical trial innovators across SDTM, ADaM, and TLF workflows

This 10-week program empowers experienced SAS programmers to master R for clinical trial analysis. Learn CDISC standards, regulatory submissions, and hands-on R toolkits such as sdtm.oak, admiral, and the nest group for end-to-end clinical trial analysis.

Duration10 weeks
Delivery50+ on-demand modules · 16 interactive live sessions
Mentorship4 expert-led Q&A sessions with course mentors
Accelerated transition path for SAS users adopting R for clinical trial analysis.
Practical application of sdtm.oak, admiral, and the nest group packages.
Emphasis on data integrity, reproducibility, and regulatory readiness.
Hands-on experience with diverse clinical trial datasets and advanced workflow walk-throughs.
Instruction led by R solution developers focused on CDISC standards.
Generating regulatory-compliant Tables, Listings, and Figures (TLFs) with the nest group.
Dashboarding and visualization mastery for stakeholder-ready insights.

Curriculum Modules

  • Key Skills

    Data integration, data manipulation, reproducible practices, clinical reporting, and interactive workshops with the experts behind these clinical trial packages.

  • Essential Data Transformation & Quality in R

    Data selection and filtering, derived variable creation, and data validation & QC using metadata-driven clinical trial analysis workflows.

  • Skills You'll Master: Data Integration & Transformation

    Merging datasets, reshaping for SDTM and ADaM compliance with sdtm.oak and admiral, understanding R data structures, and documenting governed data flows.

  • Dashboarding & Visualization Mastery

    Designing interactive dashboards, regulatory-ready visual reports, and executive narratives using R Markdown, Shiny, and htmlwidgets.

You will be able to

  • Gain proficiency converting raw clinical data into analysis-ready formats with proven R clinical trial analysis solutions.
  • Develop custom variables, aggregate study information, and run essential QC checks for reliable submissions.
  • Deliver SDTM, ADaM, and TLF outputs using sdtm.oak, admiral, and the nest group with metadata-driven transparency.

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Unlock the detailed syllabus and cohort calendar instantly.

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Additional Learning Paths

Follow-on programs that reinforce clinical trial analysis excellence.

AI for Clinical Trial Analysis

Operationalise agentic intelligence across biometrics teams

Learn to pair domain expertise with responsible generative AI. Design prompt strategies, validation guardrails, and orchestration patterns tailored to clinical operations.

Focus Areas

  • Agentic architectures & Model Context Protocol orchestration
  • Prompt engineering for SDTM, ADaM, and submission narratives
  • Compliance playbooks for AI-assisted clinical workflows

Outcomes

  • Launch AI copilots that respect clinical governance frameworks.
  • Reduce study cycle times through explainable analytics.
  • Scale collaboration between biometrics, medical writing, and data management.

Python for Clinical Trial Analysis

Engineer robust analytics pipelines for cross-functional biometrics

Design modular Python workflows covering data ingestion, anomaly detection, and regulatory reporting with an emphasis on interoperability with R and SAS ecosystems.

Focus Areas

  • Validated packaging patterns with pandas, polars, and pyarrow
  • Automated edit checks, signal detection, and review dashboards
  • APIs and SDKs that connect statistical programming toolchains

Outcomes

  • Ship reproducible Python code that complements legacy stacks.
  • Automate signal detection and data reconciliation in near real time.
  • Expose analytics services that downstream teams can trust.

Metadata-Driven Clinical Trial Analysis

Build governed metadata systems that drive analysis excellence

Establish metadata strategies that unify specs, controlled terminology, and submission assets so study analysis can thrive across programs.

Focus Areas

  • Designing metadata repositories and lineage maps
  • Linking specs to executable code for SDTM and ADaM
  • Governance, versioning, and impact analysis at scale

Outcomes

  • Break silos by turning metadata into reusable analysis assets.
  • Reduce study start-up time through ready-to-run specification templates.
  • Enable continuous compliance with transparent metadata stewardship.

Ready to bring AI LENS Academy to your organisation?

Partner with our curriculum leads to schedule private cohorts, tailor labs to your standards, and develop clinical trial analysis leaders inside your study teams.

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