In cardiovascular clinical trials, the primary endpoint often consists of multiple survival outcomes. These outcomes are commonly evaluated by a time-to-first event composite endpoint analysis. A time-to-first event analysis, however, has shortcomings in evaluating a treatment effect. It emphasizes each patient’s first event, which is often the outcome of lesser clinical importance. In addition, it ignores the severity of the events, ignores subsequent events in a patient, and clinically relevant non-survival outcomes cannot be considered. The generalized pairwise comparisons (GPC) method is an elegant statistical method for outcome analysis and reporting of prioritized composite endpoints in cardiovascular trials, as it allows flexibility in including all clinically meaningful outcomes in a single analysis. All clinically important outcomes, for example, bleeding severity, number of interventions, and patient reported outcomes, such as quality of life, can easily be implemented in a single analysis.
The treatment effect in a GPC analysis can be expressed in several measures, including the net treatment benefit, win ratio and success odds, but the win ratio has gained popularity in cardiovascular trials. These measures of treatment effect lead to equal p-values, but do not always provide the same insights into the clinical treatment effect.
In this webinar the available treatment effect estimates for GPC are reviewed, while pointing to the differences between these estimates, and suggesting recommendations for their implementation in clinical trials, in particular in the cardiovascular clinical area.
KEY TAKEAWAYS:
During this presentation, the participant will:
- Understand the differences between the GPC treatment effect measures: the net treatment benefit, success odds and win ratio
- Identify how the GPC method can be used to design clinical trials with multiple endpoints, in particular cardiovascular clinical trials
- Gain insights in the interpretation of the GPC treatment effect measures
WHO SHOULD ATTEND?
Clinicians, in particular cardiologists, trialists, statisticians
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