Understanding statistical analysis in randomized trials: tips and tricks for effective review

This review explores the critical role of statistical analysis in interpreting randomized controlled trials (RCTs), focusing on how these methods are used to evaluate the efficacy and safety of clinical interventions. RCTs are considered the gold standard in clinical research, yet their statistical complexity can make interpretation challenging. Understanding key statistical concepts, such as P-values, hazard ratios, and confidence intervals, is essential for distinguishing between statistical significance and clinical relevance. It is important to assess study design elements, including randomization methods, sample size calculations, and the handling of missing data, as these factors directly influence the validity of the findings. Additionally, the analysis plan-whether it follows an intention-to-treat approach or uses per-protocol analysis-can impact the interpretation of trial outcomes. Readers should also be aware of the distinction between pre-specified and post hoc analyses, as the latter can increase the risk of false positives. The appropriate interpretation of these elements ensures a balanced understanding of trial results, allowing clinicians and researchers to make evidence-based decisions.