This tutorial focuses on standardised mean differences (SMD) as effect masures in meta-analyses. We will explain what they are, when they should be used, how to correctly compute and interpret them, and some of the most common error made within evidence synthesis. Additionally, we have created a micro-learning module to accompany this article which includes a real-life example of meta-analysis of SMDs. Moreover, there is the opportunity to test out how to use a tool to correctly calculate SMDs, and how to interpret them. Standardised mean differences micro-learning module.
INTRODUCTION
In this tutorial, we focus on standardised meandifferences (SMD); what they are, when they should be used, how to correctly compute and interpret them, and some of the common errors made within systematic reviews.
Review authors use the SMD as a summary statistic in meta-analyses of continuous outcomes when the studies all assess the same outcome but measure it in a variety of ways [1]. For example, we can look at a meta-analysis in which included studies have measured the depressive symptoms of their participants using different scales/questionnaires [2] (e.g., the Beck Depression Inventory, the Geriatric Depression Scale, the Hamilton Rating Scale, or the Montgomery-Asberg Depression Scale). From a statistical perspective, it is virtually impossible to quantitatively synthesise the results directly. Instead, a solution would be standardising all these data to a common effect size measure: the SMD (Figure 1).