Central concepts for randomized controlled trials and other emerging trial designs

Abstract

Randomized controlled trials (RCTs) are widely considered to provide the highest-quality evidence for the comparative efficacy and safety of competing clinical strategies. The strength of using RCTs for causal inference is derived from random treatment assignment and prospective data collection. Randomization eliminates confounding at the time of treatment group assignment, achieving exchangeability of the baseline study groups, such that they are the same, on average, except for the study intervention. Prospective data collection helps ensure that eligibility assessment, treatment assignment, and the start of follow-up are aligned temporally. Temporal alignment prevents biases that are common in observational research (eg, immortal time bias). In ideal settings, the results of an RCT provide the average causal effect of the intervention on the selected outcomes in the study population. Although observational research can estimate similar causal effects, observational designs require more assumptions and more advanced analytic frameworks than an RCT designed to answer the same question. Emerging trial designs, also discussed here, seek to address certain limitations of traditional RCT designs. The purpose of this review was to provide a broad overview of the central concepts in RCT design, implementation, conduct, and data analysis.