st: flexible parametric models in small datasets

[Adam Olszewski <adam.olszewski@gmail.com>]

Hello listers,
I was wondering if anyone could clarify an issue for me. I was trying
to fit a Royston-Parmar relative survival model using -stpm2- on a
small dataset (580 observations, 43 events). The model will not
converge and depending on the number of degrees of freedom it gives
different types of errors: "cannot compute an improvement --
discontinuous region encountered" most commonly (or "initial values
not possible").
This does not happen if I leave out the relative survival option, and
goes away if I coarsen the expected mortality rate by rounding it.
I was wondering - does this indicate that there are limits on cell
sizes / degrees of freedom in such an analysis? Is there a rule (I
don't find it explicitly discussed in the literature)? RP models are
typically used for large datasets, but is there some kind of a "1
variable : 10 events" rule that can be utilized to judge when the
dataset size becomes too small?

Here is the code that will illustrate the issue (the dataset is 14KB,
I hope it is not against the list rule to post a link):

. use "http://dl.dropbox.com/u/7142569/Stats/rp.dta";
. stset surv, f(dead) exit(month60)
. stpm2 treat, df(3) scale(hazard) eform
. g roundrate=round(rate,0.01)
. stpm2 treat, df(3) scale(hazard) eform bhaz(roundrate)
. stpm2 treat, df(3) scale(hazard) eform bhaz(rate)

Thanks for any insight,

Adam Olszewski
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