A clinical trial design is the structured plan that specifies population, interventions, controls, endpoints, randomisation, blinding, and analysis to answer a clinical question. The right design minimises bias, protects participants, and delivers reliable, decision‑ready evidence for regulators, payers, and clinicians within feasible timelines and budgets. [1–3]
Observational designs (cohort, case‑control, cross‑sectional) measure associations without assigning interventions; they’re efficient for prevalence, prognosis, and hypothesis generation but limited for causal inference. Interventional designs (clinical trials) prospectively allocate treatments, enabling causal estimates under rigorous randomisation, blinding, and prespecified analyses. Choose based on question, feasibility, and ethics. [2][3]
Core elements include a clearly defined question (estimand), eligibility criteria, comparators (placebo/active), allocation concealment, blinding, endpoint hierarchy (primary/secondary/exploratory), sample size/power, and an analysis plan aligned to intercurrent events. Designs may be parallel, crossover, factorial, or cluster‑randomised depending on objectives and feasibility. [1][3]
Bias minimisers
Common structures
Adaptive features (e.g., group‑sequential looks, sample‑size re‑estimation, response‑adaptive randomisation) allow prespecified modifications using interim data while preserving validity. Pragmatic features enhance generalisability by aligning trial procedures with routine care. Use when ethics, efficiency, or heterogeneity justify flexibility and feasibility supports timely interim operations. [1][3]
Select clinically meaningful, validated primary endpoints aligned to the estimand; define secondary and safety endpoints with a multiplicity plan. Use effect size assumptions from prior evidence, control type‑I error, power (typically ≥80–90%), and anticipated attrition. Consider surrogate or composite endpoints only with justification and sensitivity analyses. [1][3]
Employ a Data and Safety Monitoring Board (DSMB) when risk or phase warrants; implement risk‑based monitoring and quality‑by‑design (QbD) to focus on errors that matter to participant safety and primary endpoints. Predefine protocol deviations, missing‑data handling, and escalation paths for safety signals. [1]
Decentralised elements (eConsent, ePROs, tele‑visits, home health, direct‑to‑patient IP) can widen access and reduce burden, but require equivalence in measurement, robust data security, clear safety workflows, and site readiness. Hybrid approaches often balance feasibility with data quality in phase II–III programs. [1]
Explanatory trials test efficacy under ideal conditions with tight controls; pragmatic trials test effectiveness in real‑world settings with broader eligibility and routine‑care procedures. Many programs blend elements depending on decision needs and feasibility. [1][3]
Use when the condition is stable, treatment effects wash out, and carryover can be minimised. It increases efficiency by letting each participant act as their own control, but it’s unsuitable for curative or irreversible outcomes. [3]
Not always. DSMBs are typical for higher‑risk interventions, large phase III trials, or adaptive platforms. For minimal‑risk studies, safety oversight may be handled by the sponsor and investigators via predefined monitoring plans. [1]