How to estimate the variance covariance matrix of a Weibull survival model manually? In flexsurv the var-covar matrix of the parameters can be produced with the vcov() function. This will produce a var-ovar matrix for the shape and scale parameters provided by the function coef(). Rather than use shape, I want to find the var-covar for m = exp(scale)^(-exp(shape)) and scale. I was wondering how I would do this?
Any help would be highly appreciated.
Edit: I'm trying to replicate some analysis done in Stata. Stata reports different parameters and so a different var-covar matrix.

Depends on whether you want an analytical answer, which I'm sure at least approximately with some series expansions or the like should be obtainable in this kind of case.
However, it's a lot simples to use simulation methods. You simply sample parameter values for shape and scale from the multivariate normal (e.g. using the mvtnorm package) for the given mean vector (i.e. the parameter estimates) and variance-covariance matrix. Then, you calculate for each sample the transformation you want to get m. Finally, you can calculate the variance covariance matrix between the resulting samples of m and the existing samples of scale (of course making sure to keep the same order of the vectors).
