The “Small” Clinical Trial: Methods, Analysis, and Interpretation in Acute Care Cardiology

Abstract

Clinical trials in acute care settings, particularly those involving small populations or high mortality contexts, present unique challenges in design and analysis. In this review we explore novel statistical approaches and methodological considerations for such trials, with a focus on cardiovascular therapies. We discuss the concept of “small” sample sizes and their limitations and cover various analytical frameworks, including frequentist and Bayesian approaches, and emphasize their implications for result interpretation and reproducibility. We examine end points such as “days alive and free specific to disease state,” which combines mortality and morbidity measures, the win ratio for hierarchical end points, and ordinal scales that capture detailed patient outcomes. These methods potentially increase statistical power and provide more clinically relevant measures compared with traditional binary outcomes; an extensive use of simulations is used to clarify this point. The use of longitudinal ordinal models is presented as a promising method to capture complex patient trajectories over time, offering insights into treatment effects at various disease stages. We also address the potential of adaptive platform trials for rare conditions, allowing for more efficient use of limited patient populations. In this overview we aim to guide researchers and clinicians in selecting optimal trial designs and analytical strategies, to ultimately improve the quality, efficiency, and interpretability of evidence in acute care cardiology.