Real-World Evidence (RWE) in Pharma: From Data to Access

Learn how to design, analyze, and apply Real-World Evidence to accelerate new drug approvals and achieve market success

What you'll learn
- Define real-world data (RWD) and real-world evidence (RWE), and explain why they matter to regulators, payers, clinicians, patients, and industry
- Identify key RWD sources (EHR, claims, registries, devices) and judge data quality and fitness for purpose
- Design fit-for-purpose real-world studies with clear estimands while preventing bias and confounding
- Apply core methods: propensity scores, survival analysis, DiD/ITS, IVs, and modern causal ML
- Build reproducible, audit-ready pipelines from raw data to results using R/Python and version control
- Prepare regulatory-grade protocols, SAPs, and traceability packages aligned to EMA/FDA expectations
- Use RWE across the product lifecycle: feasibility, label expansion, PASS, and pharmacovigilance
- Quantify value with RWE for HEOR: burden, cost-effectiveness, and budget impact to support access
- Navigate privacy, ethics, and governance (GDPR/HIPAA) and address fairness and equity in datasets
- Communicate RWE results clearly to clinical, regulatory, and payer audiences and defend decisions

Requirements
- No prior RWE experience is required. A basic grasp of clinical research/epidemiology and medical terminology helps, as does comfort with spreadsheets. Familiarity with R or Python is a plus but not necessary. You should be able to read scientific abstracts, interpret charts/tables, and have foundational statistics literacy (bias, confidence intervals, p-values, regression). A computer with reliable internet—and the ability to install free tools if desired—is recommended. Openness to privacy/ethics concepts (GDPR/HIPAA), regular study time, and curiosity will set you up for success. Course language: English.

Description
Ready to turn messy real-world data into regulatory-grade evidence?Master Real-World Evidence (RWE) for Drug Developmentis a practitioner’s course built for pharma and biotech professionals who want to move beyond slideware and ship analyses that regulators, payers, and clinicians actually trust. From day one you’ll work like an RWE team does in the wild: framing target trials, curating EHR/claims/registry data, choosing the right comparators, and stress-testing results with bias diagnostics—then defending every decision as if an FDA, EMA, or HTA reviewer were across the table.

Across ten tightly sequenced modules, you’ll progress from foundations to fluency. You’ll learn the language and taxonomy of RWD/RWE; examine landmark policies that opened the door for external controls and label expansions; and map stakeholder value so your studies answer real questions, not academic ones. You’ll get hands-on with OMOP and FHIR, build reproducible R/Python environments, and practice core methods—propensity scores, survival analysis, interrupted time-series, DiD, IVs and causal forests, quantitative bias analysis—using realistic datasets. We’ll dig into data quality and provenance, and you’ll see how inspection-ready pipelines, version control, and audit trails de-risk submissions. Because evidence must also pay its way, you’ll translate utilization and adherence into cost-effectiveness and budget-impact inputs that resonate with market access teams.

Modern RWE lives at the frontier, so we go there too: extracting endpoints from unstructured notes with transformer NLP, keeping data local with federated analytics and secure enclaves, and delivering insights back to the bedside via real-time CDS dashboards. Along the way, you’ll internalize the privacy, ethics, and governance playbook—HIPAA/GDPR, consent models, data-sharing agreements, and equity-first analytics—so your work is as responsible as it is rigorous.

Everything culminates in a capstone where you design, execute, anddefenda full RWE study to a mock regulatory panel. You’ll leave with a portfolio-ready dossier (protocol, SAP, code, diagnostics, and slides) and the confidence to lead high-stakes conversations about evidence generation, coverage, and post-marketing commitments.

If you’re a clinical scientist, HEOR/market access lead, biostatistician, data scientist, or medical affairs professional ready to make RWE your unfair advantage, enroll now and build the kind of evidence people act on.

Who this course is for:
- Biopharma professionals across Clinical Development, Medical Affairs, Pharmacovigilance, Regulatory Affairs, HEOR/Market Access, and RWE functions.
- Biostatisticians, epidemiologists, and data scientists/engineers working with health data who need RWE fluency.
- Clinicians, payers, consultants, and health-tech/analytics vendors who use, commission, or evaluate RWE.
- Graduate students and professionals switching into RWE from adjacent fields (public health, health economics, informatics).
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