Dear Nick,
thanks a lot for Your Kind reply and for Your highly appreciated
teaching-notes.
As far as the replacement of SD with SE (as You correctly pointed out)in
Gamma distribution, it is suggested by the reference I quoted in my previous
message:
Briggs A, Schulper M, Claxton K. "Decision modelling for
health economic evaluation". Oxford: Oxford University Press,
2006: 91-92.
I suppose that this replacement has to do with dealing with
the uncertainty surrounding cost parameters (even though I don't know
whether this justification is sound enough from a statistician's point of
view).
I have generated random numbers following the intention to turn the base
case "static" cost model into a probabilistic one and see what happens, in
terms of uncertainty of cost at patient level, when this parameter is driven
by a stochastic factor (I mean random sampling from Gamma distribution with
an InvGamma distribution).
You are right once again about the confusion I have made about the term
InvGamma:
I actually meant the inverse of the (cumulative) distribution function
of a gamma distribution, i.e. the quantile function
of a gamma distribution, as yielded by beta * invgammap(alpha, probability).
Thanks a lot one again for Your Kindness, time and hints.
King Regards,
Carlo
-----Messaggio originale-----
Da: [email protected]
[mailto:[email protected]] Per conto di Nick Cox
Inviato: luned� 18 giugno 2007 19.08
A: [email protected]
Oggetto: st: RE: RE: InverseGamma with beta<0
Thanks for this, which makes clearer what you did
but leaves some questions on why you did it.
Consider again this parameterisation for a two-parameter
gamma distribution:
variable x >= 0
shape parameter a > 0
scale parameter b > 0
density function [1 / (b^a gamma(a))] x^(a - 1) exp(-x / b)
For this, standard results are that
mean = a b
variance = a b^2
