Please consider the following:
In (health) decision modelling, an often-used approach is to extrapolate observed survival data with parametric functions.
Usually, several parametric models are estimated. However, depending on the data used, it can happen that one (or more) specific model is ‘not converging’.
In R, the package
flexsurvreg will give a warning message if this occurs:
Warning message: In flexsurvreg(Surv(times_start_ELd, times_end_ELd, type = "interval2") ~ : Optimisation has probably not converged to the maximum likelihood - Hessian is not positive definite.
In a report, I have seen colleagues addressing this issue as follows:
“[…] model did not converge due to the limited number of events over the long-term.”
“[…] model could not converge […]. No parametric function were estimated using these models.”
This post suggests a couple of solutions to avoid this error.
However, here I am rather interested in the implications of a non-converging model when other (alternative) models do give estimates.
My questions are:
How can you determine that the non-convergence is due to the limited number of events and not due to other causes (like massive censoring)?
Am I correct in assuming that since the problem of non-convergence often happens in only one or a few models using the same data set, this is not per se a problem of the data but rather the parametric function? (What I mean here is that I do not need to investigate the scaling of the variables used for the parametric estimation as this works fine with the majority of the models. Hence, I would not re-scale the data but rather accept the model cannot be properly estimated.)
If this non-convergence happens with one (or a few) model(s) only, is it common to ignore this model?
Thanks a lot.