Dear Lib, If you use the EVID data item (0 for observations 1 for doses) in your data set, you can denoted the observations for which you want to make a prediction by EVID=2 (other type event). That way they will not contribute to parameter estimation, but you will get predictions (both population and individual).
Best regards, Mats Mats Karlsson, PhD Professor of Pharmacometrics Dept of Pharmaceutical Biosciences Faculty of Pharmacy Uppsala University Box 591 75124 Uppsala Phone: +46 18 4714105 Fax + 46 18 4714003 www.farmbio.uu.se/research/researchgroups/pharmacometrics/<http://www.farmbio.uu.se/research/researchgroups/pharmacometrics/> From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On Behalf Of Lib Gray Sent: 19 March 2014 15:36 To: nmusers@globomaxnm.com Subject: [NMusers] Prediction with parameter estimates on new data Hello NMusers, I am new to NONMEM, and I am unsure how to use parameter estimates for prediction with new data. More specifically, I have a K/PD nonlinear mixed effects model developed in NONMEM, and I want to check its predictive power in a specific way. The data I've been using has about 500 patients, each with multiple dosing (AMT) and effect (DV) measurements. Most patients have many measurements, spanning several months. What I want to do is use the population estimates (and potentially also the between-subject variability estimates) from fitting the model to my entire data set to generate the predicted effects using only early data (for example, the first 3 months). I have the population estimates from fitting the model to all data, and I want to generate the model predicted effects using only early effect data (though with all dosing data), so that I can asses the model predicted effects for later time points. I am new to NONMEM, and am unsure if there is a way with NONMEM or PsN of using previous estimates on new data, in this specific way. I am looking into using R's deSolve package, since my model contains two differential equations, but I wanted to know if there was a more direct way. Thanks for any help, Lib Gray
