Understanding Results

Abstract

Properly conducted clinical trials provide essential evidence about the benefits and harms of a therapeutic intervention. To ensure accurate interpretation of study findings urologists should understand measures of effect and their precision. In this segment of the Users' Guide to the Urological Literature series we provide guidance on how measures of effect and precision should be interpreted and used in the evidence-based practice of urology. Using an example from the urology literature we define commonly used measures of effect and describe how these statistics can be readily generated from the results of a clinical trial. We also highlight the importance of confidence interval interpretations when critically appraising study findings. The effect of an intervention can be described in absolute or relative terms. Risk of an event, risk difference and number needed to treat are easy to interpret, and allow the patient and urologist to assess the impact of an intervention in absolute terms. Relative measures of effect (relative risk, relative risk reduction) also characterize the treatment effect and can be applied to patients with varying baseline risk. The degree of precision within which the study estimated the actual effect size may be ascertained from the associated confidence interval. Confidence intervals are also useful when interpreting the benefit and harm of an experimental intervention and, therefore, are preferred compared to p values. Urologists do not need to rely on author interpretations of the effectiveness or harmfulness of a given intervention alone, but may interpret the result of the study themselves. An understanding of measures of effect size and precision is important to the interpretation of the urological literature and facilitates evidence-based clinical practice.