In this paper, we expand the methodology presented in Mertens et. al (2020, Biometrical Journal) to the study of life-time (survival) outcome which is subject to censoring and when imputation is used to account for missing values. We consider the problem where missing values can occur in both the calibration data as well as newly - to-be-predicted - observations (validation). We focus on the Cox model. Methods are described to combine imputation with predictive calibration in survival modeling subject to censoring. Application to cross-validation is discussed. We demonstrate how conclusions broadly confirm the first paper which restricted to the study of binary outcomes only. Specifically prediction-averaging appears to have superior statistical properties, especially smaller predictive variation, as opposed to a direct application of Rubin's rules. Distinct methods for dealing with the baseline hazards are discussed when using Rubin's rules-based approaches.
翻译:在本文中,我们将Mertens等人(2020年,《生物计量学杂志》)中介绍的方法扩大到对生命期(生存)结果的研究,该研究须接受审查,而且当估算用于计算缺失的值时。我们考虑了在校准数据以及新到预测的观察(校准)中可能出现缺失值的问题。我们侧重于Cox模型。我们描述了将估算和预测性校准相结合的方法。我们讨论了对交叉校准的应用。我们展示了结论如何广泛证实仅局限于二进制结果研究的第一份文件。具体地说,预测性预测性预测性分析似乎具有较高的统计特性,特别是较小的预测性变化,而不是直接应用Rubin的规则。在使用Rubin的基于规则的方法时,讨论了处理基线危险的独特方法。