It would be better to use


(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:
Dear NMusers,

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!

Your sincerely,

Tingjie Guo

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