论文标题
贝叶斯非参数Erlang混合物模型用于生存分析
Bayesian Nonparametric Erlang Mixture Modeling for Survival Analysis
论文作者
论文摘要
我们开发了一种灵活的Erlang混合模型，用于生存分析。生存密度的模型是由Erlang密度的结构化混合物构建的，将整数形状参数与公共尺度参数混合。混合物的权重是通过在正真实线上的分布函数的增量来构建的，在正真实线上，该函数是在提前分配的。该模型具有相对简单的结构，可以平衡灵活性与有效的后验计算。此外，这意味着涉及时间依赖性混合物的危险功能的混合物表示，从而提供了危险估计的一般方法。我们扩展了模型，以处理与多个实验组相对应的生存响应，并使用定义混合物权重的特定组特定分布的依赖性dirichlet过程。讨论了模型属性，先前的规范和后验模拟，并使用合成和真实的数据示例说明了该方法。
We develop a flexible Erlang mixture model for survival analysis. The model for the survival density is built from a structured mixture of Erlang densities, mixing on the integer shape parameter with a common scale parameter. The mixture weights are constructed through increments of a distribution function on the positive real line, which is assigned a Dirichlet process prior. The model has a relatively simple structure, balancing flexibility with efficient posterior computation. Moreover, it implies a mixture representation for the hazard function that involves time-dependent mixture weights, thus offering a general approach to hazard estimation. We extend the model to handle survival responses corresponding to multiple experimental groups, using a dependent Dirichlet process prior for the group-specific distributions that define the mixture weights. Model properties, prior specification, and posterior simulation are discussed, and the methodology is illustrated with synthetic and real data examples.
