Randomized Controlled Trial (RCT)

Simple randomization assigns each participant independently (like a coin flip), which can produce group imbalances in small trials. Block randomization guarantees balanced group sizes at regular intervals by randomizing in fixed-size blocks. Stratified randomization first divides participants by key prognostic variables (e.g., age, disease severity) and then applies block randomization within each stratum, ensuring balance on those variables even in small samples.

Intention-to-treat (ITT) analysis includes every participant in the group to which they were randomly assigned, regardless of whether they completed the protocol. This preserves the prognostic balance created by randomization and gives a conservative, real-world estimate of effectiveness. Analysing only protocol-completers (per-protocol analysis) can introduce selection bias if dropout is related to treatment or outcome.

Blinding should extend as far as ethically and practically possible. Double-blind trials, in which neither participant nor assessor knows group assignment, provide the strongest protection against performance and detection bias. When blinding the participant is not feasible (e.g., surgical interventions), blinding the outcome assessor is still valuable. The term 'triple-blind' adds blinding of the data analyst. The CONSORT checklist requires explicit description of the blinding mechanism.

Choose an RCT when you need to establish causality, when randomization is ethically acceptable, and when you can control treatment delivery and follow-up. Observational designs (cohort, case-control) are appropriate when randomization is unethical or impossible, when the exposure is rare, or when very long time horizons make a trial impractical. For causal inference from observational data, methods such as propensity score matching or instrumental variables are used, but they can never fully replicate the confounding control achieved by randomization.