A super model that includes proportional hazards, proportional odds, accelerated failure time, accelerated hazards, and extended hazards models, as well as the model proposed in Diao et al. (Biometrics 69(4):840–849, 2013) accounting for crossed survival as special cases is proposed for the purpose of testing and choosing among these popular semiparametric models. Efficient methods for fitting and computing fast, approximate Bayes factors are developed using a nonparametric baseline survival function based on a transformed Bernstein polynomial. All manner of censoring is accommodated including right, left, and interval censoring, as well as data that are observed exactly and mixtures of all of these; current status data are included as a special case. The method is tested on simulated data and two real data examples. The approach is easily carried out via a new function in the spBayesSurv R package.

中文翻译：

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在六个常见生存模型中进行选择的贝叶斯因素。

包括比例风险，比例赔率，加速故障时间，加速危害和扩展危害模型以及Diao等人提出的模型的超级模型。（Biometrics 69（4）：840–849，2013）提出了将交叉生存作为特殊情况的考虑，旨在测试和从这些流行的半参数模型中进行选择。使用基于变换的伯恩斯坦多项式的非参数基线生存函数，开发了用于快速拟合和计算近似贝叶斯因子的有效方法。提供了所有类型的检查，包括右，左和间隔检查，以及可以精确观察到的数据以及所有这些的混合。作为特殊情况，包括当前状态数据。该方法在模拟数据和两个真实数据示例上进行了测试。spBayesSurv R程序包。