RE: Prediction with parameter estimates on new data
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
http://www.farmbio.uu.se/research/researchgroups/pharmacometrics/
Quoted reply history
From: [email protected] [mailto:[email protected]] On
Behalf Of Lib Gray
Sent: 19 March 2014 15:36
To: [email protected]
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