论文标题
使用rstanarm r软件包的贝叶斯生存分析
Bayesian Survival Analysis Using the rstanarm R Package
论文作者
论文摘要
生存数据在一系列学科中遇到，最著名的是健康和医学研究。尽管贝叶斯分析生存数据的方法可以带来许多好处，但与经典（例如基于似然的）方法相比，它们的使用量不多。这可能部分是由于贝叶斯生存模型的用户友好实现相对不存在。在本文中，我们描述了如何使用rstanarm r软件包来拟合广泛的贝叶斯生存模型。 RSTANARM软件包通过提供用户友好的界面（用户使用习惯R公式语法和数据帧指定其模型），并使用Stan软件（用于贝叶斯推断的C ++库）来促进贝叶斯回归建模。可以使用RSTANARM估算的模型套件是广泛的，包括通用线性模型（GLM），广义线性混合模型（GLMMS），广义添加剂模型（GAM）等。在本文中，我们仅关注生存建模功能。这包括标准参数（指数，Weibull，Gompertz）和柔性参数（基于样条）危害模型，以及标准参数加速故障时间（AFT）模型。允许所有类型的检查（左，右，间隔），以及延迟进入（左截断），随时间变化的协变量，时变效果和脆弱的效果。我们通过工作示例演示功能。我们预计这些实施将增加应用研究中贝叶斯生存分析的吸收。
Survival data is encountered in a range of disciplines, most notably health and medical research. Although Bayesian approaches to the analysis of survival data can provide a number of benefits, they are less widely used than classical (e.g. likelihood-based) approaches. This may be in part due to a relative absence of user-friendly implementations of Bayesian survival models. In this article we describe how the rstanarm R package can be used to fit a wide range of Bayesian survival models. The rstanarm package facilitates Bayesian regression modelling by providing a user-friendly interface (users specify their model using customary R formula syntax and data frames) and using the Stan software (a C++ library for Bayesian inference) for the back-end estimation. The suite of models that can be estimated using rstanarm is broad and includes generalised linear models (GLMs), generalised linear mixed models (GLMMs), generalised additive models (GAMs) and more. In this article we focus only on the survival modelling functionality. This includes standard parametric (exponential, Weibull, Gompertz) and flexible parametric (spline-based) hazard models, as well as standard parametric accelerated failure time (AFT) models. All types of censoring (left, right, interval) are allowed, as is delayed entry (left truncation), time-varying covariates, time-varying effects, and frailty effects. We demonstrate the functionality through worked examples. We anticipate these implementations will increase the uptake of Bayesian survival analysis in applied research.
