Re: model for OMEGA and SIGMA

From: Luann Phillips Subject:Re: [NMusers] model for OMEGA and SIGMA Date:Tue, 11 Feb 2003 10:13:10 -0500 NM Users, I would like to offer an alternative method for coding the Y=F*EXP(EPS(1)) error model using the 'transform-both-side' approach. $ERROR FLAG=0 IF(AMT.NE.0)FLAG=1 ;dosing records only IPRED=LOG(F+FLAG) ;transform the prediction to the log of the prediction ; IPRED=log(f) for concentration records and log(f+1) for dose records W=1 ;additive error model Y= IPRED + W*EPS(1) This will allow NONMEM to continue running when a predicted concentration of 0 occurs on any dosing record. Since predictions for dose records do not contribute to the minimum value of the objective function this change to the F (or IPRED) does not influence the outcome of the analyses. However, if code is used to alter the predicted concentration on a PK sample record the minimum value of the objective function is changed and its value can be highly dependent upon what value of IPRED is chosen as the 'new' predicted concentration. Using the above code, if NONMEM predicts a concentration of 0 on a PK sample record the run will still terminate (on some systems) with errors because LOG(0) is negative infinity. In this case, the patient ID and the observation within that patient for which the error occured will be provided. If this occurs, you may want to consider the following options: (1) Check the dosing and sampling times and the dose amounts preceding the observation for errors. Is it reasonable that a patient would have an observable concentration, given the time since last dose for the sample? (2) Is NONMEM predicting a zero concentration because of a modeled absorption lag time? Consider removing the absorption lag time or using a MIXTURE model to allow some subjects to have a lag time and others to have a lag time of zero. (3) Test a combined additive + constant CV error model (Y= F + F*EPS(1) + EPS(2)) using DV=original concentration instead of DV=log(concentration). (4) Consider temporarily excluding measured concentrations with a predicted value of zero. Work out the key components of the model and then re-introduce the concentrations. The concentrations may no longer have a predicted value of zero. (5) If none of the above works, you could switch back to the code that Vladimir suggested. Because the minimum value of the objective function will be dependent upon the 'new' value of log(F) (or log(IPRED)), I would test smaller values (-3, -5, -7, -9, etc.) until the change in minimum value of the OBJ is not statistically significant for 2 successive choices (alpha less than the values used for covariate analyses). If this is not done then any change to the model that would allow the model to predict a small non-zero value for the observation could result in a statistically significant change in the minimum value of the objective function. This type of model behavior could lead one to think that a covariate is statistically significant based upon the covariate changing the predicted value for 1 observation instead of its inclusion improving the predictions for the population in general. Regards, Luann Phillips _______________________________________________________