Pocock (2012), following Finkelstein and Schoenfeld (1999) has popularized the ``win ratio’’ as a simple method of statistical analysis for controlled clinical trials with multiple outcomes. The approach uses pairwise comparisons between patients in the treatment and control groups using a primary outcome (say time to cardiac death) with ties broken using a secondary outcome (say time to a nonfatal cardiac event). In general the observed pairwise preferences and the weight they attach to the component rankings will depend on the distribution of potential follow-up time. We present expressions for the “win” and “loss” probabilities for general bivariate survival models when follow-up is limited to a specific time horizon. In the special case of a bivariate Lehmann model we show that the win ratio does not depend on this horizon. We show how the win ratio may be estimated non-parametrically or from a parametric model. Extensions to events of three or more types are described. A novel application of the marginal method of estimation is discussed. Reference. Oakes (2016) Biometrika, 103, 742-745.