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分回归框架，用于处理带优先级的复合生存结局。该框架通过选定联系函数（包括恒等、逻辑或概率单位函数）对条件赢分进行建模，从而在统一的回归结构内处理多组分时间-事件终点。为应对右删失，我们构建了逆删失概率加权估计方程，使赢分估计在假设无删失的条件下进行。在恒等联系函数下，回归参数可表征协变量在自然赢分尺度上的关联；在逻辑联系函数下，则表征胜出的对数几率——这是一种新颖且互补的效应度量，将平局视为未胜出，施加了比胜率或胜算更保守的标准。当无平局时，逻辑赢分模型退化为比例赢分回归模型；此外，无论是否存在平局，我们估计方程的无加权版本在数值上与比例赢分点估计量一致。我们建立了所提估计量的大样本性质，并推导出考虑删失权重估计不确定性的稳健三明治方差估计量。通过不同联系函数与删失率下的广泛模拟评估有限样本性能，并通过HF-ACTION临床试验的重分析验证该方法。