Changing Landscape of Randomized Clinical Trials in Stroke: Explaining Contemporary Trial Designs and Methods

• Published in: Stroke (1970) Vol. 55; no. 11; pp. 2726 - 2730
• Main Authors: Reeves, Mathew J., Gall, Seana, Li, Linxin
• Format: Journal Article
• Language: English
• Published: Hagerstown, MD Lippincott Williams & Wilkins 01.11.2024
• Subjects: Humans; Randomized Controlled Trials as Topic - methods; Research Design; Stroke - therapy; data analysis; randomized controlled trials; data interpretation, statistical; research design; stroke
• ISSN: 0039-2499; 1524-4628; 1524-4628
• DOI: 10.1161/STROKEAHA.124.046129

Evidence generated from randomized clinical trials (RCTs) plays an indispensable role in advancing clinical stroke care. Although the number of stroke-related RCTs published every year has grown exponentially over the past 25 years, the execution and completion of RCTs, particularly those conducted in a hyperacute setting, have grown more complicated and challenging over the years. In addition to the practical challenges associated with conducting a clinical trial, like obtaining human subjects approval, identifying clinical sites, training trial personnel, and enrolling the target number of patients within the available funding and timeline, the complexity of contemporary RCT designs and analyses has become much more exacting. It is no longer sufficient to have a decent understanding of the 2-arm, placebo-controlled RCT, combined with a rudimentary grasp of the P value; things are now much more complicated. Innovations in trial design and analysis, including adaptive, Bayesian, platform, and noninferiority designs, have occurred to address the problems of poor trial efficiency. However, these advances require the end user to have a much greater level of understanding regarding the rationale, conduct, analysis, and interpretation of each design. While these newer designs seek greater efficiency, there are inevitably tradeoffs that need to be understood. In this month's edition of Stroke, we introduce a new series designed to help fill in these knowledge gaps. Over the next few months, 4 papers will be published that address major design innovations (adaptive, Bayesian, platform, and noninferiority) with the aim of illustrating how these approaches can make trials more efficient (where efficiency is defined as getting to the right answer, sooner, with a potentially lower sample size). In addition to introducing this series, this current article also reviews traditional hypothesis testing and the common misinterpretations of the P value; fortunately, new philosophical schools of inference are beginning to vanquish the overreliance on the P value. We are excited about the opportunity to educate the Stroke readership about these new trial designs and the profound implications that they bring.