Prioritizing clinically important outcomes using the win ratio

Composite outcomes are composed of two or more distinct “component” events (for example, heart attacks, death) and are often analyzed using time-to-first event methods ignoring the relative importance of the component events. When using the win ratio, component events are instead placed into a hierarchy from most to least important; more important components can then be prioritized over less important outcomes (for example, death, followed by myocardial infarction). The method works by first placing patients into pairs. Within each pair, one evaluates the components in order of priority starting with the most important until one of the pair is determined to have a better outcome than the other.

 

A major advantage of the approach is its flexibility: one can include in the hierarchy outcomes of different types (for example, time-to-event, continuous, binary, ordinal, and repeat events). This can have major benefits, for example by allowing assessment of quality of life or symptom scores to be included as part of the outcome. This is particularly helpful in disease areas where recruiting enough patients for a conventional outcomes trial is unfeasible.

The win-ratio approach is increasingly popular, but a barrier to more widespread adoption is a lack of good statistical software. The calculation of sample sizes is also complex and usually requires simulation. We present winratiotest, the first package to implement win-ratio analyses in Stata. The command is flexible and user-friendly. Included in the package is the first software (we know of) that can calculate the sample size for win-ratio-based trials without requiring simulation.