Gustafson Paul, Aeschliman Dana, Levy Adrian R
Department of Statistics, University of British Columbia, Vancouver, BC, Canada.
Lifetime Data Anal. 2003 Mar;9(1):5-19. doi: 10.1023/a:1021821002693.
There has been much recent work on Bayesian approaches to survival analysis, incorporating features such as flexible baseline hazards, time-dependent covariate effects, and random effects. Some of the proposed methods are quite complicated to implement, and we argue that as good or better results can be obtained via simpler methods. In particular, the normal approximation to the log-gamma distribution yields easy and efficient computational methods in the face of simple multivariate normal priors for baseline log-hazards and time-dependent covariate effects. While the basic method applies to piecewise-constant hazards and covariate effects, it is easy to apply importance sampling to consider smoother functions.
最近有许多关于贝叶斯生存分析方法的研究，这些方法纳入了诸如灵活的基线风险、随时间变化的协变量效应和随机效应等特征。一些提出的方法实施起来相当复杂，而我们认为通过更简单的方法可以获得同样好甚至更好的结果。特别是，面对基线对数风险和随时间变化的协变量效应的简单多元正态先验，对数伽马分布的正态近似产生了简单有效的计算方法。虽然基本方法适用于分段常数风险和协变量效应，但应用重要性抽样来考虑更平滑的函数很容易。
