Re: addition of dummy points

From: "Pravin Jadhav" pravinj@gmail.com Subject: Re: [NMusers] addition of dummy points Date: Thu, April 21, 2005 4:52 pm Hi Leonid, Thanks for the reply. I looked at the estimates and the predictions from two runs (w and w/o dummy point). As I said earlier, the estimates are different. However, the predictions are identical (I mean, making exactly 45 degree angle). So I thought your following comment applies to this situation: I was having trouble convincing myself that the model is over-parametrized or there is any identifiability issue. Because, all I am doing is the addition of a few dummy points that --conceptually-- DO NOT even count during estimation. Could it be a system problem? So I ran a small experiment. All the codes, datasets and results of which are available at http://www.geocities.com/pravin1851/nmtest/ I simulated data for one subject after 100 mg i.v. blous dose (clearance=0.2 L/hr, V=1 L, additive residual variability=2 mg/L). A total of 9 PK samples were obtained at 1,2,4,6,8,10,12,14,18 hr. ( http://www.geocities.com/pravin1851/nmtest/onecomp.xls) Then, I appended the above dataset with 50 dummy points and MDV=1. ( http://www.geocities.com/pravin1851/nmtest/onecomp_app.xls) Both dataset were analyzed using ADVAN1, ADVAN6 and ADVAN9 with identical initial conditions. CTL files are also available on that link. (onecomp_adv*.ctl and onecomp_adv*_app.ctl for the respective datasets) And the estimates are surprisingly different! ( http://www.geocities.com/pravin1851/nmtest/nmmbt.xls) #Run Obj CL V SIG1 onecomp_adv1 10.888 0.206 1 1.10905 onecomp_adv1_app 10.888 0.206 1 1.10905 onecomp_adv6 10.889 0.546 2.65 1.10905 onecomp_adv6_app 10.882 0.522 2.54 1.10905 onecomp_adv9 10.913 0.55 2.68 1.11355 onecomp_adv9_app 10.882 0.522 2.54 1.10905 However, the predictions are identical. Here is a plot PREDs for each dataset, ADVAN1 is the reference. (EXACTLY MY PROBLEM- different estimates- same pred) ( http://www.geocities.com/pravin1851/nmtest/pred_comparison.wmf) Does that mean this model is over-parametrized? I don't think so. If this dataset was blinded, any of those estimates and fits look okay to me. I will have no idea which one to believe in real life. So it is something else. As Nick mentioned in the previous post- something about step size. Please note that ADVAN1 implementing closed form solution yields TRUE estimates. But why should it make such a large difference? Please correct me, if there is any conceptual mistake in implementing these simulation. I also noticed in this problem as well as my dataset that the ratio of the KEY parameters remains the same. I look forward to hearing from you. Pravin -- Pravin Jadhav Graduate Student Department of Pharmaceutics MCV/Virginia Commonwealth University DPE1/OCPB/CDER/Food and Drug Administration Phone: (301) 594-5652 Fax: (301) 480-3212