I would like help understanding why a survival regression with no censored data-points does not give the same variance estimates as a linear model (see code below). I think it must be something to do with the fact that the variance is an actual parameter in the survival version via the log(scale), and possibly that different assumptions are made about the distribution of the variance. But I really don't know, I'm just guessing. The reason I ask is because I am moving a process, that has always been modelled using a linear model, to a survival model (because there are sometimes a few censored data points). In the past, the censored data points have been treated as missing which imparts bias. The variance of the estimates in this process is key, so I need to know why they are changing in this systematic way?! library(survival) ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14) ctl.surv <- Surv(ctl) trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69) lmod <- lm (ctl ~ trt ) smod <- survreg(ctl.surv ~ trt,dist="gaussian") coef(lmod) coef(smod) # same vcov(lmod) vcov(smod) # smod is smaller diag(vcov(lmod)) / diag(vcov(smod))[1:2] # 1.25 == 0.5*(n/(n-1)) ( summary(lmod)$coef [ ,"Std. Error"] / summary(smod)$table[1:2,"Std. Error"] )^2 # 1.25 = 0.5*(n/(n-1)) [[alternative HTML version deleted]]