How to calculate confidence intervals for the scale parameter of a parametric survival model? I use R code to create the following AFT model
survregLogNormal <- survreg(Surv(futime, fustat) ~ ecog.ps + rx, ovarian, dist = "loglogistic")

Next, I want to calculate the confidence interval of the estimated parameters / model coefficients with
confint( survregLogNormal  )

What I receive is the following output:
                 2.5 %    97.5 %
(Intercept)  3.9390699 8.3829255
ecog.ps     -1.3888480 0.7167439
rx          -0.3510217 1.7600539

Interestingly, the CI for the scale parameter is not part of the output. Is there any way in R to make confint() to return also the CI for the scale parameter, which one can see in the model's summary:
Call:
survreg(formula = Surv(X, pain) ~ sex, data = mydata, dist = "loglogistic")
             Value Std. Error     z      p
(Intercept)  5.639      0.153 36.96 <2e-16
sex          0.300      0.179  1.67  0.095
Log(scale)  -1.221      0.134 -9.14 <2e-16

Scale= 0.295 

...


According to the survreg.object manual page, the var component of an object returned by survreg() is "the variance-covariance matrix for the parameters, including the log(scale) parameter(s)." So the square root of the diagonal element  for log(scale) in that var component gives its standard error, and you use the standard 1.96 factor for converting standard error limits to 95% CI under the assumption of asymptotic normality (as for the other parameter estimates: those are z-values shown).
It's possible for a survreg() function call to specify a fixed value of scale instead of estimating it from the data. In that case there would be no standard error for it; perhaps that's why confint() doesn't directly return a CI value.
