treatment of BQL

From: "Gibiansky, Leonid" <GibiansL@globomax.com> Subject: treatment of BQL Date: Mon, 4 Oct 1999 11:37:58 -0400 Dear NONMEM users, I'd like to experiment with the way how NONMEM treats observations below quantification limit (BQL). The idea is the following: if the observation (DV) is BQL, and the model prediction (F) is BQL, then the program should not penalize for the difference (if any) (F-DV). I've tried the following: IF(DV.LT.BQL .AND. F .LE. BQL) IPRED=DV Y=IPRED*(1+ERR(1))+ERR(2) This works and gives reasonable results. However, I am concerned that it may alter the distributions of ERR(1) and ERR(2) is there are many BQL measurements. Could you shear your thoughts about this way of treating BQL measurements ? Thanks ! Leonid