Singini I L, Mwambi H G, Gumedze F N
Department of Statistical Sciences, University of Cape Town, Cape Town, South Africa.
Department of Statistics, School of Mathematics, Statistics and Computer Science, University of KwaZulu Natal, Durban, South Africa.
Commun Stat Simul Comput. 2023;52(11):5163-5177. doi: 10.1080/03610918.2021.1995751. Epub 2021 Oct 26.
A two-stage joint survival model is used to analyse time to event outcomes that could be associated with biomakers that are repeatedly collected over time. A Two-stage joint survival model has limited model checking tools and is usually assessed using standard diagnostic tools for survival models. The diagnostic tools can be improved and implemented. Time-varying covariates in a two-stage joint survival model might contain outlying observations or subjects. In this study we used the variance shift outlier model (VSOM) to detect and down-weight outliers in the first stage of the two-stage joint survival model. This entails fitting a VSOM at the observation level and a VSOM at the subject level, and then fitting a combined VSOM for the identified outliers. The fitted values were then extracted from the combined VSOM which were then used as time-varying covariate in the extended Cox model. We illustrate this methodology on a dataset from a multi-centre randomised clinical trial. A multi-centre trial showed that a combined VSOM fits the data better than an extended Cox model. We noted that implementing a combined VSOM, when desired, has a better fit based on the fact that outliers are down-weighted.
两阶段联合生存模型用于分析与随时间重复收集的生物标志物相关的事件发生时间结局。两阶段联合生存模型的模型检验工具有限，通常使用生存模型的标准诊断工具进行评估。这些诊断工具可以改进并实施。两阶段联合生存模型中的时变协变量可能包含异常观测值或个体。在本研究中，我们使用方差转移异常值模型（VSOM）来检测两阶段联合生存模型第一阶段中的异常值并降低其权重。这需要在观测水平和个体水平上拟合VSOM，然后为识别出的异常值拟合一个组合VSOM。然后从组合VSOM中提取拟合值，并将其用作扩展Cox模型中的时变协变量。我们在一项多中心随机临床试验的数据集上说明了这种方法。一项多中心试验表明，组合VSOM比扩展Cox模型更适合该数据。我们注意到，在需要时实施组合VSOM，基于异常值权重降低的事实，拟合效果更好。
