Hi Kevin, I am sorry for the confusion. The lower boundary (lb_ij) refers to the lower theoretical boundary for PRED (the population typical prediction). For most PK models this is 0 (concentrations can’t be negative). In this cases the equation can be simplified. Note that the lower boundary is for the predictions and lower boundary’s for observations (i.e. lloq and llod) is not relevant for this parameter.
More often for PD models the theoretical lower boundary is different from 0 (e.g. -1 for relative change from baseline or positive values for some scores). In these cases the lb_ij parameter becomes important to take into account. I hope this answers you question. Kind regards, Martin Bergstrand, Ph.D. Partner and Senior Consultant Pharmetheus AB +46(0)709 994 396 martin.bergstr...@pharmetheus.com www.pharmetheus.com > 28 apr. 2018 kl. 15:19 skrev Wang Kevin <fengdubianb...@hotmail.com>: > > Hi All, > > I’m trying to understand what pcVPC did in PsN. > When I reading below paper, > “Martin Bergstrand, Andrew C. Hooker, Johan E. Wallin, and Mats O. Karlsson > Prediction-Corrected Visual Predictive Checks for Diagnosing Nonlinear > Mixed-Effects Models” > I got confused about the meaning of lb_ij(lower boundary) on equation (1). > > pcY_ij=lb_ij+(Y_ij-lb_ij)*(pred_bin-lb_ij)/(pred_ij-lb_ij) > Yij = observation or prediction for the ith individual and jth time point, > pcYij = prediction-corrected observation or prediction, > PREDij = typical population prediction for the ith individual and jth time > point, > and PReEDbin = median of typical population predictions for the specific bin > of independent > variables. > > For example, if a pk model was simulated with 3 different dose and the time > is real time not nominal time. > How to calculate lb_ij? > > Below is a pcVPC simulated data example (not real) > ID > DV > TIME > strata_no > DOSE > 1 > 2.0 > 0.90 > 1 > 1 > 1 > 12.6 > 3.10 > 1 > 1 > 1 > 2.8 > 5.00 > 1 > 1 > 1 > 1.5 > 8.00 > 1 > 1 > 1 > 1.0 > 12.00 > 1 > 1 > 2 > 1.0 > 0.90 > 2 > 2 > 2 > 22.3 > 6.10 > 2 > 2 > 2 > 12.0 > 5.10 > 2 > 2 > 2 > 3.0 > 8.30 > 2 > 2 > 2 > 1.0 > 12.00 > 2 > 2 > 3 > 1.0 > 1.00 > 3 > 3 > 3 > 40.1 > 3.10 > 3 > 3 > 3 > 11.7 > 5.40 > 3 > 3 > 3 > 6.6 > 8.00 > 3 > 3 > 3 > 2.0 > 12.00 > 3 > 3 > > Any help or suggestion is appreciated. > Thanks in advance. > > Regards > > Kevin
