BACKGROUND: Adverse event incidence analyses are a critical component for describing the safety profile of any new intervention. The results typically are presented in lengthy summary tables. For therapeutic areas where patients have frequent adverse events, analysis and interpretation are made more difficult by the sheer number and variety of events that occur. Understanding the risk in these instances becomes even more crucial. PURPOSE: We describe a space-saving graphical summary that overcomes the limitations of traditional presentations of adverse events and improves interpretability of the safety profile. METHODS: We present incidence analyses of adverse events graphically using volcano plots to highlight treatment differences. Data from a clinical trial of patients experiencing an aneurysmal subarachnoid hemorrhage are used for illustration. Adjustments for multiplicity are illustrated. RESULTS: Color is used to indicate the treatment with higher incidence; bubble size represents the total number of events that occur in the treatment arms combined. Adjustments for multiple comparisons are displayed in a manner to indicate clearly those events for which the difference between treatment arms is statistically significant. Furthermore, adverse events can be displayed by time intervals, with multiple volcano plots or animation to appreciate changes in adverse event risk over time. Such presentations can emphasize early differences across treatments that may resolve later or highlight events for which treatment differences may become more substantial with longer follow-up. LIMITATIONS: Treatment arms are compared in a pairwise fashion. CONCLUSIONS: Volcano plots are space-saving tools that emphasize important differences between the adverse event profiles of two treatment arms. They can incorporate multiplicity adjustments in a manner that is straightforward to interpret and, by using time intervals, can illustrate how adverse event risk changes over the course of a clinical trial.
BACKGROUND: Adverse event incidence analyses are a critical component for describing the safety profile of any new intervention. The results typically are presented in lengthy summary tables. For therapeutic areas where patients have frequent adverse events, analysis and interpretation are made more difficult by the sheer number and variety of events that occur. Understanding the risk in these instances becomes even more crucial. PURPOSE: We describe a space-saving graphical summary that overcomes the limitations of traditional presentations of adverse events and improves interpretability of the safety profile. METHODS: We present incidence analyses of adverse events graphically using volcano plots to highlight treatment differences. Data from a clinical trial of patients experiencing an aneurysmal subarachnoid hemorrhage are used for illustration. Adjustments for multiplicity are illustrated. RESULTS: Color is used to indicate the treatment with higher incidence; bubble size represents the total number of events that occur in the treatment arms combined. Adjustments for multiple comparisons are displayed in a manner to indicate clearly those events for which the difference between treatment arms is statistically significant. Furthermore, adverse events can be displayed by time intervals, with multiple volcano plots or animation to appreciate changes in adverse event risk over time. Such presentations can emphasize early differences across treatments that may resolve later or highlight events for which treatment differences may become more substantial with longer follow-up. LIMITATIONS: Treatment arms are compared in a pairwise fashion. CONCLUSIONS: Volcano plots are space-saving tools that emphasize important differences between the adverse event profiles of two treatment arms. They can incorporate multiplicity adjustments in a manner that is straightforward to interpret and, by using time intervals, can illustrate how adverse event risk changes over the course of a clinical trial.
Authors: Denong Wang; Roopa Bhat; Raymond A Sobel; Wei Huang; Lai-Xi Wang; Tomas Olsson; Lawrence Steinman Journal: Drug Dev Res Date: 2014-03-11 Impact factor: 4.360
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Authors: Suzie Cro; Prakash Patel; Jonathan Barker; David A Burden; Christopher E M Griffiths; Helen J Lachmann; Nick J Reynolds; Richard B Warren; Francesca Capon; Catherine Smith; Victoria Cornelius Journal: Trials Date: 2020-02-10 Impact factor: 2.279