From: Toufigh GordiSubject: [NMusers] Fixed or estimated? Date:Fri, 28 Mar 2003 13:57:24 -0500 Dear all, I have a simple PK/PD model of cell binding. I worked on the PK and then fixed the PK parameters and got PD estimates. Then I estimated everything all together. The new estimates (when everything is free) are not identical to when PK parameters were fixed, although they are very similar. The question is what parameters to present, and what is the criteria for choosing the results from one model (e.g. fixed PK) over another (nothing fixed). Different plots from the two runs are very similar. As one might expect, OFV is lower in the "free" model (decreased by 11, #PK parameters=9). My second question is whether one can use a decrease in OFV as a guide for choosing the model in this case. How much should the drop be to imply a significant advantage of the "free" model? Does this model have 9 more parameters compared to a fixed PK model (which would require a much larger drop in OFV than 10 to be significantly better)? Personally, I would present the results from a model where nothing is fixed. However, I would appreciate some discussion on the matter. Toufigh Gordi _______________________________________________________ From: "Bachman, William" Subject:RE: [NMusers] Fixed or estimated? Date: Fri, 28 Mar 2003 14:49:03 -0500 It turns out that OFV is a less than ideal criterion for goodness of fit and I never use it as the SOLE determinant for model discrimination. Also take into consideration the goodness of fit plots and the magnitude of the variance parameters. _______________________________________________________ From: Leonid Gibiansky Subject:Re: [NMusers] Fixed or estimated? Date:Fri, 28 Mar 2003 15:00:15 -0500 Toufigh Gordi This is win-win situation, no matter what you decide you have good a model that describe both PK and PK/PD. You may consider similarity of the parameters as a confirmation that your overall model is good. Which one to choose depend on your goals. In your case, they are nearly identical, so it does not matter what you do. In general, the "fixed parameters" model provides the best fit for the PK data. For the PD data, this model uses PK model predictions and PD data to describe PK/PD relationship. "Free parameters" model may provide slightly worth fit for the PK data with slightly better fit for the PD data (and better overall fit). You should not count PK parameters into the PD model (more precisely, you should count parameters even if they are fixed). Difference of 11 points evidence that both models are roughly equivalent, but they have the same number of the parameters if you count fixed one as well. If you need the model to describe PK data only, I would not add PD data there (because incorrectly chosen PK/PD model may damage otherwise good PK model). If you would like to describe PD data, use the best model (with free parameters). Similarity of two models evidence that PD data do not disturb PK fit, providing additional comfort. Good luck, Leonid _______________________________________________________ From:Liping Zhang Subject:Re: [NMusers] Fixed or estimated? Date:Friday, March 28, 2003 2:34 PM -0700 Dear Toufigh, I agree with Dr. Gibiansky's recommendation completely. If you are interested in PD model, an additional reason for using the all-para-free model is that when you simulate (the ultimate goal of modeling), that is the model you will use. If you use the fixed-PK model analysis results to simulate, then you are not really simulating from the same model as your data-analytic model. About the OFV, I agree with Dr. Bachman, too. For the fixed-PK analysis, you did not indicate whether you included BOTH PK and PD data to estimate PD when you fixed the PK para to estimate PD. If you do not include both, there is no comparison between two OFVs. Best regards, Liping Zhang _______________________________________________________