We propose a generalized win fraction regression framework for prioritized composite survival outcomes. The framework models the conditional win fraction through a chosen link function (including identity, logit, or probit), thereby accommodating multi-component time-to-event endpoints within a unified regression structure. To handle right censoring, we construct inverse-probability-of-censoring-weighted estimating equations that target the win fraction as if censoring were absent. Under the identity link, regression parameters characterize covariate associations on the natural win fraction scale. Under the logit link, they characterize the log odds of winning -- a new and complementary effect measure that treats ties as failures to win, imposing a more conservative standard than the win ratio or win odds. When there are no ties, the logit win fraction model reduces to proportional win fraction regression; moreover, the unweighted version of our estimating equations numerically coincides with the proportional win fraction point estimator regardless of ties. We establish large-sample properties of the proposed estimators and derive a consistent sandwich variance estimator that accounts for uncertainty from the estimated censoring weights. Extensive simulations examine finite-sample performance across link functions and censoring rates, and our method is illustrated through a reanalysis of the HF-ACTION clinical trial.

翻译：我们提出了一种广义赢率回归框架，用于处理优先顺序复合生存结局。该框架通过选定的链接函数（包括恒等、logit或probit）对条件赢率进行建模，从而在统一的回归结构中容纳多分量时间-事件终点。为处理右删失，我们构建了基于逆删失概率加权的估计方程，使赢率估计如同没有删失一般。在恒等链接下，回归参数表征协变量在自然赢率尺度上的关联；在logit链接下，则表征获胜的对数比值——这是一种新颖且互补的效应度量，将平局视为未获胜，施加了比赢率比或获胜几率更严格的标准。当无平局时，logit赢率模型退化为比例赢率回归；此外，无论是否存在平局，我们的无加权估计方程在数值上与比例赢率点估计量一致。我们建立了所提估计量的大样本性质，并推导了考虑删失权重估计不确定性的稳健夹心方差估计量。通过大量模拟检验了不同链接函数和删失率下的有限样本表现，并通过重新分析HF-ACTION临床试验数据展示了方法的实用性。