We consider a parametric modelling approach for survival data where covariates are allowed to enter the model through multiple distributional parameters, i.e., scale and shape. This is in contrast with the standard convention of having a single covariate-dependent parameter, typically the scale. Taking what is referred to as a multi-parameter regression (MPR) approach to modelling has been shown to produce flexible and robust models with relatively low model complexity cost. However, it is very common to have clustered data arising from survival analysis studies, and this is something that is under developed in the MPR context. The purpose of this article is to extend MPR models to handle multivariate survival data by introducing random effects in both the scale and the shape regression components. We consider a variety of possible dependence structures for these random effects (independent, shared, and correlated), and estimation proceeds using a h-likelihood approach. The performance of our estimation procedure is investigated by a way of an extensive simulation study, and the merits of our modelling approach are illustrated through applications to two real data examples, a lung cancer dataset and a bladder cancer dataset.
翻译:我们认为,对于生存数据,允许共变体通过多种分布参数(即规模和形状)进入模型的参数建模方法,允许共变体进入模型,这与具有单一的共变依赖参数的标准公约(通常为规模)形成对照。采用所谓的多参数回归(MPR)模型方法,已经证明产生了灵活和稳健的模型,模型复杂性成本相对较低。然而,从生存分析研究中得出的数据是十分常见的,这是在模型中正在开发的。本条款的目的是扩大多变体模型,通过在规模和形状回归组成部分中引入随机效应,处理多变体生存数据。我们考虑了这些随机效应(独立、共享和关联)可能的各种依赖结构,并用类似方法估算了收益。我们估算程序的业绩通过广泛的模拟研究的方式进行调查,通过对两个真实数据实例、肺癌数据集和膀胱癌数据集的应用来说明我们的模拟方法的优点。