MULTIVARIATE SURVIVAL ANALYSIS USING COX REGRESSION-MODEL
MULTIVARIATE SURVIVAL ANALYSIS USING COX REGRESSION-MODEL
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DOI：
10.1002/hep.1840070628
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发表时间：
1987-11-01
期刊：
影响因子：
13.5
通讯作者：
CHRISTENSEN, E
中科院分区：
文献类型：
--
作者：
CHRISTENSEN, E
FIG. 1. Estimated cumulative survival probability sit)[Kaplan-Meier plot (811 (top) and estimated cumulative hazard A (t)(bottom) for the survival data presented in Table 1. One can esttrnate the one from the other using the relations: A (t)=-loGS (t) and S (t)= e-,’(”. hazard, a slight rise to a low hazard. It appears from the curve that the hazard is high initially and less thereafter. A cumulative survival curve and the cumulative hazard curve derived from it are summarizing descriptions concerning the studied total group of individuals. However, there may be a wide variation in the survival time (and hazard) between individual subjects. Although the curves illustrate the variation among the subjects, they do not allow identification of who had a long survival (low hazard) and who had a short survival (high hazard).COVARIATES To make such an identification possible or to allow prediction of survival time in individual subjects, it is necessary to identify and utilize variables couarying with survival. For example, it may be that serum albumin at the starting point covaries with the subsequent survivai time; ie, in subjects with a low albumin, the survival time may be short (hazard high), and in subjects with a high albumin, the survival time may be long (hazard low). If the covariation (or correlation) between the level of albumin and the survival time is large, the level of albumin may to some degree “explain” the variation in survival time or hazard between the subjects (11). In that case, the level of serum albumin in a new subject may to some degree be used to predict his/her survival time or hazard. In a controlled clinical trial, the treatment given may be an important covariate which may “explain” a difference in survival between the treatment groups.
