In almost all clinical settings, patients are at risk for multiple potential events and, in consultation with health-care providers, must weigh the potential benefits and harms across these events when making decisions. As researchers seek to build an evidence base to inform these decisions, they must contend with a choice as to how they will handle the different events. One approach, arguably the standard approach in the literature, is to consider the events individually by conducting analyses and publishing results for each one at a time. Doing so, however, fails to acknowledge or exploit the inherent multivariate nature of the data, represents a lost opportunity, and results in an evidence base that is not aligned with how clinical decision-making is actually performed. The article by Prentice et al. (Am J Epidemiol. 2020;189(9):972-981) in this issue of the Journal moves beyond this standard by illustrating recently developed methods that directly take advantage of information on the co-occurrence of multiple events. Moreover, their article highlights the role of modern methods in deriving additional information and insight from studies of multiple clinical outcomes by making full use of multivariate data, with the goal being to complement, not replace, existing methods.
Keywords: decision-making; multivariate data analysis; semicompeting risks; time-to-event data.
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