Wei L J
Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115.
Stat Med. 1992 Oct-Nov;11(14-15):1871-9. doi: 10.1002/sim.4780111409.
For the past two decades the Cox proportional hazards model has been used extensively to examine the covariate effects on the hazard function for the failure time variable. On the other hand, the accelerated failure time model, which simply regresses the logarithm of the survival time over the covariates, has seldom been utilized in the analysis of censored survival data. In this article, we review some newly developed linear regression methods for analysing failure time observations. These procedures have sound theoretical justification and can be implemented with an efficient numerical method. The accelerated failure time model has an intuitive physical interpretation and would be a useful alternative to the Cox model in survival analysis.
在过去二十年中,Cox比例风险模型被广泛用于检验协变量对失效时间变量的风险函数的影响。另一方面,加速失效时间模型,即简单地将生存时间的对数对协变量进行回归,在截尾生存数据的分析中很少被使用。在本文中,我们回顾了一些新开发的用于分析失效时间观测值的线性回归方法。这些方法有合理的理论依据,并且可以用一种有效的数值方法来实现。加速失效时间模型有直观的物理解释,在生存分析中可能是Cox模型的一个有用替代方法。