Real-world evidence

This branch covers real-world evidence for medical writers, divided into definitions and data sources, uses across a medicine’s lifecycle, drug safety, regulatory assessment, analysis and AI, data integrity and reporting. It leaves real-world elements within interventional trials, device surveillance and evidence about diet and lifestyle exposures to neighbouring topics.

• What real-world data and evidence are, and what they add — definitions, big data, source strengths and limitations, linked data, evidence generation, comparisons with trials and the writer’s role
• Patient registries as a source of evidence — purposes and design, data collection, sponsorship and ethics, quality and analysis, uses and rare-disease registries
• Patient reports in social media as safety evidence — suspected adverse reactions, report validity, studies of postings, collection channels and methodological gaps
• Real-world evidence across a medicine’s lifecycle — early and clinical development, uses after launch and examples linking trial findings with routine care
• Real-world evidence in drug safety — surveillance after approval, long-term effects, excluded populations, rare events and the evidence’s place beside trials
• Real-world evidence and the regulators — EMA and FDA programmes and presenting studies for regulatory assessment
• Analysing real-world data — bias reduction, regression, propensity scores, causal inference, Bayesian methods and machine learning
• Artificial intelligence in real-world evidence — data preparation and analysis, generative AI in trial design, algorithm outputs, bias, opacity and reporting
• Word bans and the integrity of real-world data — US federal terminology and data removals, standardised terms, translated patient-reported outcomes and professional responses
• Writing about a real-world study — audience and study details, ethics and anonymity, transparent reporting, causal language, strengths and limitations