In studies with survival endpoints, it is often of interest to predict the disease risk or survival probabilities in the presence of censored failure times. One commonly used approach is to model the association between the survival outcome and covariates via a semiparametric regression model and use the fitted model for prediction. In this article, we propose two methods to evaluate or predict the survival rates. The first method estimates survival probabilities by matching survival functions, and the second one is based on matching censored quantiles. Unlike traditional regression approaches, the proposed methods directly match the distribution of linear combinations of the covariates to the entire target distribution or parts of it. To accommodate censoring, we adopt a redistribution-of-mass technique for the proposed matching censored quantiles. The asymptotic consistency of the resulting estimators is well established. Simulation studies and an example with real data are also provided to further illustrate the practical utilities of our proposals. The proposed methods have been implemented in an R package.

中文翻译：

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生存数据的匹配分布

在具有生存终点的研究中，在存在审查失败时间的情况下预测疾病风险或生存概率通常是有意义的。一种常用的方法是通过半参数回归模型对生存结果和协变量之间的关联进行建模，并使用拟合模型进行预测。在本文中，我们提出了两种评估或预测存活率的方法。第一种方法通过匹配生存函数来估计生存概率，第二种方法基于匹配删失分位数。与传统的回归方法不同，所提出的方法直接将协变量的线性组合的分布与整个目标分布或其部分进行匹配。为了适应审查，我们对提议的匹配删失分位数采用了质量再分配技术。得到的估计量的渐近一致性已经很好地建立了。还提供了模拟研究和带有真实数据的示例，以进一步说明我们建议的实际用途。所提出的方法已在 R 包中实现。