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.
翻译:在一系列学科,主要是健康和医学研究中都发现了生存数据。虽然贝耶斯人对生存数据的分析方法可以提供若干好处,但比古典(例如基于可能性的)方法使用得较少。这可能部分是由于相对缺乏对巴耶斯人生存模型的方便用户的实施。在本篇文章中,我们描述了如何利用Rarstanarm R软件包来适应一系列广泛的巴耶斯人生存模型。Rstanarm软件包便利了巴耶斯人回归模型的建模,提供了方便用户的接口(用户使用传统的R公式语法和数据框架来指定其模型),以及使用斯坦软件(一个用于Bayesian Infiration的C+ 图书馆)来进行后端估计。可以用Rayesian生存模型进行估算的成套模型范围很广,包括通用的线性混合模型(GLMM)、通用的添加模型(GMM)和更多。在文章中,我们只侧重于生存模型的功能。这包括标准参数(实验性、Webuil、Gompertzeral ) 和软性时间-IFServial 运行模型,这些模型作为标准的加速运行。