4 replies to this topic

[Crystal]

Dear all,

I built a POP PK model, after I finished Predicitive check, I found the output picture was a little strange, the name of Y-axis was "DV0" instead of "DV". Does anyone know why there was a "0" behind the "DV"?

Thanks a lot.

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[Simon Davis]

Crystal, can you post your project, or at least your model code please?  Simon

[Crystal]

Crystal, can you post your project, or at least your model code please?  Simon

Sure, here is the model code:

test(){

deriv(A1 = - (Cl * C) + (Aa_P1 * Ka_P1) + (Aa_P2 * Ka_P2))

urinecpt(A0 = (Cl * C))

deriv(Aa_P1 = - (Aa_P1 * Ka_P1))

deriv(Aa_P2 = - (Aa_P2 * Ka_P2))

C = A1 / V

dosepoint(Aa_P1, bioavail = (F_P1), idosevar = Aa_P1Dose, infdosevar = Aa_P1InfDose, infratevar = Aa_P1InfRate)

error(CEps = 0.01)

observe(CObs = C * (1 + CEps))

dosepoint(Aa_P2, tlag = (Tlag_P2), bioavail = (1-F_P1), idosevar = Aa_P2Dose, infdosevar = Aa_P2InfDose, infratevar = Aa_P2InfRate)

stparm(V = tvV * Weight^dVdWeight * exp(dVdRACE1*(RACE==1)) * exp(nV))

stparm(Cl = tvCl * Weight^dCldWeight * BMI^dCldBMI * exp(dCldRACE1*(RACE==1)) * exp(nCl))

stparm(Ka_P1 = tvKa_P1 * Weight^dKa_P1dWeight * exp(dKa_P1dRACE1*(RACE==1)) * exp(nKa_P1))

stparm(Ka_P2 = tvKa_P2 * Weight^dKa_P2dWeight * exp(dKa_P2dRACE1*(RACE==1)) * exp(nKa_P2))

stparm(F_P1 = tvF_P1 * Weight^dF_P1dWeight * exp(dF_P1dRACE1*(RACE==1)) * exp(nF_P1))

stparm(Tlag_P2 = tvTlag_P2 * Weight^dTlag_P2dWeight * exp(dTlag_P2dRACE1*(RACE==1)) * exp(nTlag_P2))

Edited by Crystal, 24 October 2022 - 07:01 AM.

[smouksassi1]

DV0 is just DV observed i.e your original data the blue points are those original data (pred corrected if you used this option)

 

so nothing special here 

 

you might want to add the CI in your vpc plot you also might want to look at it versus TAD . pred corrected split by study / study phases

[Crystal]

thank you very much for your reply~~

DV0 is just DV observed i.e your original data the blue points are those original data (pred corrected if you used this option)

 

so nothing special here 

 

you might want to add the CI in your vpc plot you also might want to look at it versus TAD . pred corrected split by study / study phases