Key result
Repeat-event analyses improve outcome evaluation over conventional time-to-first-event methods across five CV trials.
Why the study?
Many cardiovascular randomized trials have repeat nonfatal events but commonly use time-to-first event as the primary outcome, raising questions about the value of repeat event analyses.
Analyzing recurrent events rather than just time-to-first event can provide a better understanding of treatment effect and total disease burden in cardiovascular trials.
Many randomized trials in cardiovascular disease have repeat nonfatal events (such as hospitalizations) occurring during patient follow-up; yet, it remains common practice to have time-to-first event as the primary outcome. We explore the value of analyses that include repeat events. Do they help us understand the effect of treatment and total disease burden? Do they enhance statistical power? Should they become a trial's primary analysis? It may also be difficult to choose which of the various statistical methods for analyzing repeat events to use, and we provide a nontechnical guide to what each method is doing. We compare several methods for repeat events: Lin Wei Yang Ying, negative binomial, joint frailty, win ratio, and area under the curve. We illustrate their performance in 5 large cardiovascular trials and compare them with time-to-first-event analyses. We review their use in recently published heart failure trials and make recommendations for their use in future trials.
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Gregson et al. (2023) conducted a review in Cardiovascular disease. Statistical methods for analyzing repeat events vs. Time-to-first-event analyses was evaluated. Statistical methods for analyzing repeat events were compared with time-to-first-event analyses across 5 large cardiovascular trials to evaluate their performance and utility.
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