Designing and analysing clinical trials
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A clinical trial’s design and analysis determine which treatment question it can answer and how far its findings apply. The parts divide by the choices involved: establishing efficacy, defining the treatment effect, enrolling a population, incorporating alternative trial methods, handling missing data and planning analysis, with a separate part for disease- and product-specific settings. Choosing among study designs, statistical tests and models, data governance, evidence from routine care, and writing the protocol or regulatory dossier belong to neighbouring concerns.
- Establishing that a treatment works — controlled evidence, endpoints, treatment effects, clinical meaning and benefit–risk, and the distinction between efficacy and effectiveness
- Estimands and intercurrent events — defining the treatment effect, choosing strategies for intercurrent events and collecting the data they require
- Trial population representativeness — generalisability, underrepresentation, population differences, enrolment requirements and strategies, subgroup analysis and where trials run
- Decentralised, pragmatic and real-world elements in trials — trial methodologies, external controls and hybrid designs, participant needs, data fitness and access, privacy and oversight
- Missing data — quantity and patterns, prevention by design, missingness assumptions, analysis methods, sensitivity analyses and reporting
- Statistical analysis planning and reporting — prespecifying the question, power and sample size, methods, programming and departures from the plan, and reporting an auditable analysis
- Trials in particular settings — design and endpoint choices in cancer, Alzheimer’s disease, rare diseases, vaccines, allergen immunotherapy and biosimilars