Sample Size Estimation in Clinical Trials

Abstract

Sample size estimation remains as a cornerstone in the meticulous planning and execution of clinical trials, pivotal for ensuring studies possess the requisite statistical power to discern meaningful treatment effects. Insufficient sample sizes compromise the robustness of findings, whereas excessively large samples inflate costs and compromise data integrity. This article meticulously explains the multifaceted factors that outline sample size determination, encompassing various factors such as research design, types of hypotheses, error thresholds, effect size considerations, validity and precision. It investigates into the scope of methodologies available for sample size computation, spanning from intricate statistical formulas to pragmatic tabular approaches. Moreover, it underscores the significance of post-hoc power analysis in retrospectively evaluating completed studies, shedding light on their statistical robustness. This literature review furnishes a nuanced understanding of the sample size estimation landscape in clinical trials, delineating their strengths, limitations, and real-world applications. Anticipating participant attrition assumes paramount importance for proactively adjusting sample sizes, ensuring studies remain methodologically sound. Equipped with a profound grasp of these principles, researchers are empowered to conduct scientifically rigorous and impactful clinical trials, furnishing compelling evidence to inform judicious decision-making in healthcare interventions.

Keywords

• Sample size
• Statistically significant
• Clinically significant
• Type I error
• Type II error
• Power