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Re: [NMusers] ETAs & SIGMA in external validation

From: Jakob Ribbing <>
Date: Fri, 6 Apr 2018 19:31:52 +0200

Dear Tingjie,

If I understand your description correctly, you would like to evaluate =
the published model (and point estimates of population parameters) using =
GoF plots (residual-error and eta-plots), rather than via simulation =
(e.g. VPC or PPC)?
At least for the latter it would be necessary to constrain individual =
parameters from (zero and) negative space (for parameters which must be =
The solution you initially implemented will bias the parameter =
distribution severely, since only values greater than or equal to the =
typical parameter value is allowed.

For estimation you can add NOABORT to the $ESTIMATION line. Right after =
the individual parameter has been assigned its value you can check that =
it is positive:
PARA = TVPARA * (1+ETA(1))
IF (PARA.LE.0) EXIT 1 23

In simulation mode, you can instead draw a new eta in subjects that have =
a negative parameter value.
I have written some code for you below, but please check for any typos =
Also, notice this is an example which avoids negative parameter values =
for a single parameter, but you can implement the same solution with =
multiple individual parameters in the DO WHILE block.

Also, before you go ahead and try to fix anything related to etas in =
estimation: Check that the code and data you have put in place is =
The first subject that fails with a negative parameter value: Can you =
find anything particular in your dataset for this individual?
For example, you may have included zero DV values in your data set, or =
you may have coded a missing covariate as -99.

Finally, the residual error is usually much larger than the assay error, =
inflated by e.g. adherence and errors in sample collection, imperfect =
model, etc.
Many things may change from one study to the next. A well controlled =
study (or in some cases a better assay) could result in lower residual =
More commonly, changes in population or inclusion criteria may change =
IIV in parameters (as well as typical or population values).
However, as a starting point for your external evaluation, it may be =
good to assume that all population parameters are the same as in the =
published model, both fixed and random effects.

Best wishes


PARA = TVPARA * (1+ETA(1))

;Sampling etas until the new subject has Para>0
    PARA = TVPARA * (1+ETA(1))
;Etas that do not need resampling should be declared after the above DO =
WHILE block. They should follow below


The SIMETA requires an additional seed number, see nmhelp for more info

Jakob Ribbing, Ph.D.

Senior Consultant, Pharmetheus AB

Cell/Mobile: +46 (0)70 514 33 77 <>

Phone, Office: +46 (0)18 513 328

Uppsala Science Park, Dag Hammarskjölds väg 52B

SE-752 37 Uppsala, Sweden


Received on Fri Apr 06 2018 - 13:31:52 EDT

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