[The relevance and irrelevance of charts in clinical research]

Charts are a visual aid that is used in articles in order to highlight the results of an investigation. They allow illustrating the results with the purpose of making them clearer. Charts, just like statistical tests, are selected based on the objective of the study, the types of variable, and the statistical analyzes to be illustrated. Some of the most commonly used charts in clinical practice are frequency histograms, which illustrate qualitative variables or frequencies; also error charts, that are used for normally distributed quantitative variables; box plots or violin plots are used for distribution-free quantitative variables, and survival curves are for variables that include the person-time variable. The aforementioned charts can be used to illustrate the comparisons between maneuvers and outcome depending on the type of variable that is being analyzed. When two groups are compared and the dependent variable is dichotomous, forest plots are used; for multivariate models, the chart depends on the type of analysis. As for logistic regression and linear regression, tree diagrams are used; and scatter plots are used for linear regression. Survival plots are used for Cox proportional hazards. Although charts can be very useful, if they are misused, they can show differences where there are none, which leads to a misinterpretation of the studies. In this article, we will use examples to complement the topics that were previously addressed in the articles of this series.