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Serious and chronic health conditions such as cardiovascular diseases (CVDs) are posing challenges to the health system. Recently clinical trials in these fields have focused on composite endpoints that take into account both disease-related mortality and major disease-related morbidity events. It is the time to the first component of the composite endpoint experienced by a patient that is the traditional study endpoint and treatment aims are to delay the time to the first event and to reduce its frequency. As the name implies, the time-to-first composite event analysis approach focuses only on the first composite event and ignores subsequent events. For a chronic disease, this can lead to a substantial loss of potentially important information. For instance, in chronic heart failure (HF) studies, the traditional composite endpoint of HF-related hospitalizations and CVD death will ignore CVD deaths that are preceded by HF-related hospitalizations. This paper explores the limitations of the traditional time-to-first event approach and discusses the potential value of incorporating all events. The authors argue that endpoints capturing recurrent event information can lead to interpretable measures of treatment effect that better reflect disease burden than traditional time-to-first event endpoints by using the available information beyond the first event. This paper aims to raise awareness of the value and potential pitfalls of alternative treatment effect measures to facilitate meaningful cross-functional conversations among trialists and other stakeholders such as regulators, payers, and treating physicians who all are striving to the same goal-to deliver the most effective treatments to patients.