It would be better to use
$EST METHOD=1 INTERACTION MAXEVAL=0
(at least if the original model was fit with INTERACTION option and
residual error model is not additive).
One option is to use Para = THETA * EXP(ETA)
You would be changing the model, but the model is not too good any way
if you need to restrict Para > 0 artificially.
SIGMA should be taken from the model.
On 4/6/2018 12:32 PM, Tingjie Guo wrote:
I have two questions regarding the statistical model when performing
external validation. I have a dataset and would like to validate a
published model with POSTHOC method i.e. $EST METHOD=0 POSTHOC MAXEVAL=0.
1. The model added etas in proportional way, i.e. Para = THETA * (1+ETA)
and this made the posthoc estimation fail due to the negative individual
parameter estimate in some subjects. I constrained it to be positive by
adding ABS function i.e. Para = THETA * ABS(1+ETA), and the estimation
can be successfully running. I was wondering if there is better workaround?
2. OMEGA value influences individual ETAs in POSTHOC estimation. Should
we assign $SIGMA with model value or lab (where external data was
determined) assay error value? If we use model value, it's
understandable that $SIGMA contains unexplained variability and thus it
is a part of the model. However, I may also understand it as that model
value contains the unexplained variability for original data (in which
the model was created) but not for external data. I'm a little confused
about it. Can someone help me out?
I would appreciate any response! Many thanks in advance!