How to Understand Measures of Effect in Clinical Research: Practical interpretation and Application

In clinical research, assessing the association between two variables is a critical and fundamental task. Clinical studies aim to establish the effect size of the exposure to a variable on a given outcome. To measure this effect size, various statistical measures are used, among the most common are the prevalence ratio (PR), the relative risk (RR), the odds ratio (OR), the hazard ratio (HR), the incidence rate ratio (IRR), the attributable risk (AR), the number needed to treat (NNT), the mean difference (MD), and the linear regression coefficient (β). Each of these measures has its advantages and limitations, and their choice depends on the type of study and the nature of the data being analyzed. Therefore, it is important to understand the interpretation and use of each of them to perform an appropriate analysis. In this article, our goal is to explain in a practical way how to interpret these measures and how to use their p-values and 95% confidence intervals to assess statistical inference. Understanding how to evaluate the association between two variables is crucial for the design and analysis of high-quality clinical studies. This enables evidence-based decision-making and promotes improvements in patient care.