Available online 17 October 2022

Abstract

Randomized controlled trials (RCT) 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 temporally aligned. Temporal alignment prevents biases that are common in observational research (e.g., 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 Seminars review is to provide a broad overview of the central concepts in RCT design, implementation, conduct, and data analysis.

Introduction

Well-designed randomized controlled trials (RCT) are widely considered to provide the highest quality evidence for the efficacy and safety of competing clinical strategies.1 An RCT is an experimental construct where participants are randomly assigned to one of two or more clinical interventions and prospectively monitored to ascertain the effects of the intervention(s) on relevant outcomes of interest. Random assignment creates subsets of participants that are comparable in all aspects on average (i.e., exchangeable) except for the study intervention. Randomization eliminates all confounding, both obvious and unrecognized, at the time of treatment group assignment, achieving exchangeability at baseline.2

In ideal settings with full treatment protocol adherence and complete data capture throughout the entire follow-up period, the results of an RCT can provide the average causal effect of the intervention on the selected outcomes in the target population. Although observational research can estimate similar causal effects, casual inference with observational data typically requires more advanced analytic frameworks, assumptions, and background knowledge than an RCT.3 While RCTs are not universally free of bias, their inherent features including randomization, prospective design, and well-established best-practice norms make RCTs the gold-standard for most comparative effectiveness research. Emerging trial designs and frameworks for observational analyses seek to address the limitations of traditional RCT designs, enhancing our ability to achieve scientific advancement through a range of evidence sources. The purpose of this Seminars review is to provide a broad overview of the central concepts in RCT design, implementation, conduct, and data analysis.

Section snippetsRandomized Controlled Trial Design

Clinical trials are typically designed to address the prevention, detection, or treatment of disease. Prevention trials aim to limit the development of a selected disease process through interventions like behavior modification, screening strategies, vaccination or medication initiation and assessing the interval effect on mitigation of disease. Treatment trials are those that assess the efficacy of a therapy (e.g., medication initiation, procedural or surgical intervention) on a selected

Reporting

Once a study design has been finalized and prior to subject enrollment, all clinical trials are required by the ICMJE (International Committee of Medical Journal Editors) to be registered in a publicly accessible database as a condition of the publication of research results generated by the trial, the largest registry being Clinicaltrials.gov.23 Additionally, most prominent medical journals require adherence to the CONSORT (Consolidated Standards of Reporting Trials) Statement, a 25-item

Limitations of Traditional RCTs

While RCTs play a central role in determining which clinical interventions work best, they have some important limitations. Equipoise is needed to compare interventions, and ethical considerations can severely restrict the types of questions which can be answered by trials. RCTs are often limited in their sample size due to the substantial resource requirements needed to enroll and follow participants over time. Consequently, they are typically only able to address one or two causal questions

Sequential Multiple Assignment Randomized Trial (SMART)

A SMART is a randomized clinical trial design with multiple randomizations that aims to mimic the sequential nature of clinical decision-making—prescribing an initial treatment, assessing response to treatment and possibly prescribing continued treatment, changing treatments, or augmenting the initial treatment.26–28 Sequential clinical decision-making is captured in four key SMART design components: (1) prespecified key decision points, (2) prespecified intervention alternatives to be

Conclusions

As the field of vascular surgery continues to advance, our community will be repeatedly motivated to rethink paradigms for clinical knowledge generation. Balancing feasibility, internal validity, generalizability, transportability, prohibitive funding concerns and ethical considerations will remain a formidable challenge. Randomized trials will likely continue to be the gold standard for causal inference into the near future; however, we must remain open to innovative options beyond those

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

1. Murad MH, Asi N, Alsawas M, Alahdab F. New evidence pyramid. Évid Based Medicine. 2016;21(4):125-127. doi:10.1136/ebmed-2016-110401

2. Manson JE, Shufelt CL, Robins JM. The Potential for Postrandomization Confounding in Randomized Clinical Trials. Jama. 2016;315(21):2273-2274. doi:10.1001/jama.2016.3676

3. Hernán MA, Robins JM. Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available. Am J Epidemiol. 2016;183(8):758-764. doi:10.1093/aje/kwv254

4. Juszczak E, Altman DG,

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