Bayesian model selection and averaging in additive and proportional hazards models.
Bayesian model selection and averaging in additive and proportional hazards models.
复制标题
加性和比例风险模型中的贝叶斯模型选择和平均。
DOI：
10.1007/s10985-004-0384-x
复制
发表时间：
2005
影响因子：
1.3
通讯作者：
Herring,AmyH
中科院分区：
文献类型：
--
作者：
Dunson,DavidB;Herring,AmyH
Although Cox proportional hazards regression is the default analysis for time to event data, there is typically uncertainty about whether the effects of a predictor are more appropriately characterized by a multiplicative or additive model. To accommodate this uncertainty, we place a model selection prior on the coefficients in an additive-multiplicative hazards model. This prior assigns positive probability, not only to the model that has both additive and multiplicative effects for each predictor, but also to sub-models corresponding to no association, to only additive effects, and to only proportional effects. The additive component of the model is constrained to ensure non-negative hazards, a condition often violated by current methods. After augmenting the data with Poisson latent variables, the prior is conditionally conjugate, and posterior computation can proceed via an efficient Gibbs sampling algorithm. Simulation study results are presented, and the methodology is illustrated using data from the Framingham heart study.
