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[NMusers] inclusion of covariates with $PRIOR

From: Anna Chan Kwong <anna.chankwong_at_gmail.com>
Date: Thu, 16 May 2019 10:46:11 +0200

Dear NMusers

I am wondering about the inclusion of covariates with the $PRIOR subroutine=
.

The article "Use of Prior Information to Stabilize a Population Data
Analysis" (Gisleskog, Karlsson, Beal 2002) states that Stepwise Covariate
Modelling (SCM) is possible on a parameter estimated with prior
information, under conditions :

1) Population parameters have to be centered around the prior geometric
mean (often the median) of the covariate (for example, if the power
function is used: (COV/medianCOV)**THETA(COV), medianCOV is the median in
the prior dataset)
Is it correct to use functions like linear function
(1+THETA(COV)*(COV-medianCOV) or exponential function
(exp(THETA(COV)*(COV-medianCOV) ?

2) the SUM of the objective function and the PRIOR penalty should be used
to perform hypothesis tests.

Could you confirm I have properly understood this condition??
I am in doubt because automated SCM with $PRIOR in PsN (
https://uupharmacometrics.github.io/PsN/docs.html) compares the "OBJECTIVE
FUNCTION VALUE WITHOUT CONSTANT" (without PRIOR penalty).


3) hypothesis tests such as the Likelihood Ratio Test needs to be performed
with the ACTUAL significance level

Is there a way to determine the actual significance level faster than
Stochastic Simulation and Estimation?

4) the prior omega of the parameter on which the covariate impacts should
be decreased by the product of THETA(COV)² and the prior population
variance of log(COV).
Does that mean we should manually adjust the $OMEGAP value of a parameter
on which we test the covariate ? OMEGAP(adjusted) = OMEGAP -
(THETA(COV))²*var

with OMEGAP = prior OMEGA estimate of the parameter on which the covariat=
e
is added ; var = prior population variance of log COV

Thank you very much for your understanding,

Sincerely yours,

Anna Chan Kwong
PhD sudent in Pharmacometrics, Marseille University.


Received on Thu May 16 2019 - 04:46:11 EDT

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