The majority of methods available to model survival data only deal with right censoring. However, there are many applications where left, right and/or interval censoring simultaneously occur. A methodology that is capable of handling all types of censoring as well as flexibly estimating several types of covariate effects is presented. The baseline hazard is modelled through monotonic P-splines. The model’s parameters are estimated using an efficient and stable penalised likelihood algorithm. The proposed framework is evaluated in simulation, and illustrated using an original data example on time to first hospital infection or in-hospital death in cirrhotic patients. A peak of risk in the first week since hospitalisation is identified, together with a non-linear effect of Model for End-Stage Liver Disease (MELD) score. The GJRM R package, with an implementation of our approach, is freely available on CRAN.

中文翻译：

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混合审查下基于链接的生存可加模型评估医院获得性感染的风险

大多数可用于对生存数据建模的方法仅处理右删失。然而，有许多应用同时发生左、右和/或区间删失。提出了一种能够处理所有类型的审查以及灵活估计几种类型的协变量效应的方法。基线风险通过单调 P 样条建模。该模型的参数是使用有效且稳定的惩罚似然算法估计的。提议的框架在模拟中进行评估，并使用原始数据示例说明肝硬化患者首次住院感染或院内死亡的时间。确定住院后第一周的风险峰值，以及终末期肝病模型 (MELD) 评分的非线性效应。GJRM R 包，