From:"GORDI, TOUFIGH [R&D/0437]"
Subject:[NMusers] A simple question Date:Tue, 18 Jun 2002 02:29:57 -0400 Hi! I have noticed that a common practice to validate a model is to apply the model based on a previous data set to a new set of data, fixing all model parameters and see how the model describes the new data. This is specially done when the model does not estimate the parameters properly on the new data set. Could someone explain for me how it is possible that the model does not generate good estimates from a data set but still can describe the same data set fairly well when its parameters are fixed based on previous findings? To me it sounds as that there are several equally well estimations of model parameters, choice of the "correct estimates" being dependent on how reliable the estimates are in relation to known physiological values. Thank you. Toufigh Gordi, ******* From:"LSEN (Lars Erichsen)" Subject: RE: [NMusers] A simple question Date:Tue, 18 Jun 2002 10:23:40 +0200 Hi Toufigh, The situation you describe may occur when the new data do not contain as much information as the previous data on which parameter estimates are based. With the new data only you may not be able to estimate all the model parameters because they lack some information, e.g. if new data are from a smaller dose range compared to previous data. BR Lars Erichsen