Functional win-fractions regression models for composite outcomes

In clinical trials with prioritized composite outcomes, win ratios are commonly employed to evaluate the efficacy of investigational interventions. Adjusting such win ratios with respect to covariates enhances the accuracy and precision of treatment effect estimates, by controlling for baseline clinical characteristics, demographics, etc. Effects of the covariates on composite outcomes are often of complex and non-linear nature, reflecting the complexity of underlying biological mechanisms. Parametric approaches that assume an additive effect of covariates on the log-hazard often fail to capture such complexities, resulting in unreliable inferences and reduced predictive accuracy. In this article, we introduce a flexible win fraction regression framework based on \(\hbox {B}\) -splines, that is capable of assessing the extent and nature of the functional effects of covariates on the outcome adaptively. By leveraging the moment condition model framework equipped with the generalized method of moments (GMM) technique, we develop an efficient computational algorithm to carry out inference based on the proposed model. Using the asymptotic distribution of the estimated spline coefficients, we develop a large-sample test to assess the significance of the functional covariates. The favorable operating characteristics of the proposed methodology, compared to the current state-of-the-art, are assessed through extensive simulations. Finally, the practical utility of our proposal is demonstrated through the analysis of composite time-to-event datasets arising from cardiovascular and breast cancer clinical trials.