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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

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