RE: inclusion of covariates with $PRIOR
Hi Anna,
That you want to explore covariate relationships on a parameter suggests that
you believe your data contain plenty of information about the parameter.
Therefore do you really need to use a prior on the parameter(s) in question? A
little more info around what your “base” model is and why you use a prior could
help answering.
You write “"OBJECTIVE FUNCTION VALUE WITHOUT CONSTANT" (without PRIOR penalty).
”, but the CONSTANT mentioned is not related to the prior, but rather the term
of the OFV that is related to the number of observations only (Nobs*LN(2*PI)).
For obtaining a reference distribution for the likelihood ratio test a
randomization (permutation) test is often useful as it uses the real data as
opposed to simulated data. PsN functionality for this is “randtest”.
Best regards,
Mats
Quoted reply history
From: [email protected] <[email protected]> On Behalf Of
Anna Chan Kwong
Sent: den 16 maj 2019 10:46
To: [email protected]
Subject: [NMusers] inclusion of covariates with $PRIOR
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://urlproxy.sunet.se/canit/urlproxy.php?_q=aHR0cHM6Ly91dXBoYXJtYWNvbWV0cmljcy5naXRodWIuaW8vUHNOL2RvY3MuaHRtbA%3D%3D&_s=bWF0cy5rYXJsc3NvbkBmYXJtYmlvLnV1LnNl&_c=ef5b1054&_r=dXUtc2U%3D)
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 covariate 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.
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