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 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.
