Pseudo‐observations based on the nonparametric Kaplan‐Meier estimator of the survival function have been proposed as an alternative to the widely used Cox model for analyzing censored time‐to‐event data. Using a spline‐based estimator of the survival has some potential benefits over the nonparametric approach in terms of less variability. We propose to define pseudo‐observations based on a flexible parametric estimator and use these for analysis in regression models to estimate parameters related to the cumulative risk. We report the results of a simulation study that compares the empirical standard errors of estimates based on parametric and nonparametric pseudo‐observations in various settings. Our simulations show that in some situations there is a substantial gain in terms of reduced variability using the proposed parametric pseudo‐observations compared with the nonparametric pseudo‐observations. The gain can be measured as a reduction of the empirical standard error by up to about one third; corresponding to an additional 125% larger sample size. We illustrate the use of the proposed method in a brief data example.

中文翻译：

---

使用参数伪观测的回归模型。

已经提出了基于生存函数的非参数Kaplan-Meier估计量的伪观测，作为广泛使用的Cox模型的替代方法，用于分析审查的事件时间数据。就变异性较小而言，使用基于样条的生存期估计量比非参数方法有一些潜在的好处。我们建议基于灵活的参数估计量来定义伪观测，并将其用于回归模型中的分析以估计与累积风险相关的参数。我们报告了一项模拟研究的结果，该研究比较了在各种设置下基于参数和非参数伪观测的估计的经验标准误差。我们的仿真表明，在某些情况下，与非参数伪观测相比，使用拟议的参数伪观测可以减少可变性。可以将增益测量为经验标准误差最多减少三分之一。对应于额外的125％更大的样本量。我们在一个简短的数据示例中说明了所提出方法的使用。