In time to event data analysis, it is often of interest to predict quantities such as t-year survival rate or the survival function over a continuum of time. A commonly used approach is to relate the survival time to the covariates by a semiparametric regression model and then use the fitted model for prediction, which usually results in direct estimation of the conditional hazard function or the conditional estimating equation. Its prediction accuracy, however, relies on the correct specification of the covariate-survival association which is often difficult in practice, especially when patient populations are heterogeneous or the underlying model is complex. In this paper, from a prediction perspective, we propose a disease-risk prediction approach by matching an optimal combination of covariates with the survival time in terms of distribution quantiles. The proposed method is easy to implement and works flexibly without assuming a priori model. The redistribution-of-mass technique is adopted to accommodate censoring. We establish theoretical properties of the proposed method. Simulation studies and a real data example are also provided to further illustrate its practical utilities.

中文翻译：

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通过匹配审查数据的分位数估计来预测疾病风险

在事件数据分析中，经常需要预测诸如t的数量年生存率或连续时间的生存功能。常用的方法是通过半参数回归模型将生存时间与协变量相关联，然后使用拟合模型进行预测，这通常可以直接估计条件危害函数或条件估计方程。但是，其预测准确性依赖于协变量与生存关联的正确规范，而这在实践中通常很困难，尤其是在患者人群异质或基础模型复杂的情况下。在本文中，从预测的角度出发，我们通过将协变量的最佳组合与分布时间相匹配来提出一种疾病风险预测方法。所提出的方法易于实现并且灵活地工作，而无需假设先验模型。采用质量重新分配技术来适应审查。我们建立了该方法的理论性质。还提供了仿真研究和实际数据示例，以进一步说明其实际用途。