Background: Composite outcomes, which combine multiple types of clinical events into a single outcome, are common in clinical trials. The usual analysis considers the time to first occurrence of any event in the composite. The major criticisms of such an approach are (1) this implicitly treats the outcomes as if they were of equal importance, but they often vary in terms of clinical relevance and severity, (2) study participants often experience more than one type of event, and (3) often less severe events occur before more severe ones, but the usual analysis disregards any information beyond that first event. Methods: A novel approach, referred to as the win ratio, which addresses the aforementioned criticisms of composite outcomes, is illustrated with a re-analysis of data on fatal and non-fatal cardiovascular disease time-to-event outcomes reported for the Multiple Risk Factor Intervention Trial. In this trial, 12,866 participants were randomized to a special intervention group ( n = 6428) or a usual care ( n = 6438) group. Non-fatal outcomes were ranked by risk of cardiovascular disease death up to 20 years after trial. In one approach, participants in the special intervention and usual care groups were first matched on coronary heart disease risk at baseline and time of enrollment. Each matched pair was categorized as a winner or loser depending on which one experienced a cardiovascular disease death first. If neither died of cardiovascular disease causes, they were evaluated on the most severe non-fatal outcome. This process continued for all the non-fatal outcomes. A second win ratio statistic, obtained from Cox partial likelihood, was also estimated. This statistic provides a valid estimate of the win ratio using multiple events if the marginal and conditional survivor functions of each outcome satisfy proportional hazards. Loss ratio statistics (inverse of win ratios) are compared to hazard ratios from the usual first event analysis. A larger 11-event composite was also considered. Results: For the 7-event cardiovascular disease composite, the previously reported first event analysis based on 581 events in the special intervention group and 652 events in the usual care group yielded a hazard ratio (95% confidence interval) of 0.89 (0.79–0.99), compared to 0.86 (0.77–0.97) and 0.91 (0.81–1.02) for the severity ranked estimates. Results for the 11-event composite also confirmed the findings of the first event analysis. Conclusion: The win ratio analysis was able to leverage information collected past the first experienced event and rank events by severity. The results were similar to and confirmed previously reported traditional first event analysis. The win ratio statistic is a useful adjunct to the traditional first event analysis for trials with composite outcomes.
Modeling past event feedback through biomarker dynamics in the multistate event analysis for cardiovascular disease data
