RE: treatment of BQL

Date: Tue, 05 Oct 1999 15:17:40 +0100 From: James <J.G.Wright@ncl.ac.uk> Subject: RE: treatment of BQL Dear Leonid, How many iterations it takes your model to converge is the least of your worries in this situation. The model you have described is almost certainly inconsistent because you have created an objective function where predicting BQL when you have a BQL observation is far more desirable than predicting a value near to it (The discontinuity matters. A lot). Essentially, you have created a situation where the BQL observations are extremely highly weighted. If the model does manage to predict above QL, then you are simply carrying out single imputation. The BQL observations are the least well-determined in the sample and this uncertainty needs to be acknowledged. Imputing values (or a distribution) influenced by the other data is a sensible way to approach this and avoid the heavy-tail phenomena. Chopping terms out of your objective function in an ad hoc manner does not correspond to "using the inequality in the objective function". The approach you describe still suffers from the other problems I described in my first mail. You can never accomodate uncertainty when the model would like to predict above QL with this approach, no matter what methodology you are using. The problems I have raised are not NONMEM specific. James