Abstract The recent results of Cardiovascular Outcomes Trials (CVOTs) in type 2 diabetes have clearly established the cardiovascular (CV) safety or even the benefit of two therapeutic classes, Glucagon-Like Peptide-1 receptor agonists (GLP-1 RA) and Sodium-Glucose Co-Transporter-2 inhibitors (SGLT-2i). Publication of the latest CVOTs for these therapeutic classes also led to an update of ESC guidelines and ADA/EASD consensus report in 2019, which considers using GLP-1 RA or SGLT-2i with proven cardiovascular benefit early in the management of type 2 diabetic patient with established cardiovascular disease (CVD) or at high risk of atherosclerotic CVD. The main beneficial results of these time-to event studies are supported by conventional statistical measures attesting the effectiveness of GLP-1 RA or SGLT2i on cardiovascular events (absolute risk, absolute risk difference, relative risk, relative risk reduction, odds ratio, hazard ratio). In addition, another measure whose clinical meaning appears to be easier, the Number Needed to Treat (NNT), is often mentioned while discussing the results of CVOTs, in order to estimating the clinical utility of each drug or sometimes trying to establish a power ranking. While the value of the measure is admittedly of interest, the subtleties of its computation in time-to-event studies are little known. We provide in this article a clear and practical explanation on NNT computation methods that should be used in order to estimate its value, according to the type of study design and variables available to describe the event of interest, in any randomized controlled trial. More specifically, a focus is made on time-to-event studies of which CVOTs are part, first to describe in detail an appropriate and adjusted method of NNT computation and second to help properly interpreting NNTs with the example of CVOTs conducted with GLP-1 RA and SGLT-2i. We particularly discuss the risk of misunderstanding of NNT values in CVOTs when some specific parameters inherent in each study are not taken into account, and the following risk of erroneous comparison between NNTs across studies. The present paper highlights the importance of understanding rightfully NNTs from CVOTs and their clinical impact to get the full picture of a drug’s effectiveness.