RE: [NMusers] OMEGA selection

[Mark Sale - Next Level Solutions]

Nick et al.     At this risk of starting an discussion that probably has little mileage left in it.  First I agree with Nick on covariance - it probably doesn't matter.  But, I'd like to point out what may be an error in our logic.  We content that we have demonstrated that covariance doesn't matter.  Our evidence is that, when bootstrapping, the parameters for the sample that have successful covariance are not different from those that failed.  So, we conclude that the results are the same regardless of covariance outcome across sampled data sets - the independent variable in this test is the data set, the model is fixed. In model selection/building, we have a fixed data set and the independent variable is the model structure.   Whether covariance success is a useful predictor across different models with a fixed data set is a different question than whether covariance is a useful predictor across data sets with a fixed model. But, in the end, I do agree that biological plausibility, diagnostic plots, reasonable parameters and some suggestion of numerical stability/identifiably (such as bootstrap CIs) are more important than a successful covariance step. Mark Mark Sale MD Next Level Solutions, LLC www.NextLevelSolns.com 919-846-9185 -------- Original Message -------- Subject: Re: [NMusers] OMEGA selection From: Nick Holford <n.holf...@auckland.ac.nz> Date: Wed, April 15, 2009 12:17 pm To: nmusers@globomaxnm.com Ethan, Do not pay any attention to whether or not the $COV step runs or even if the run is 'SUCCESSFUL' to conclude anything about your model. Your opinion is not supported experimentally e.g. see http://www.mail-archive.com/nmusers@globomaxnm.com/msg00454.html for discussion and references. NONMEM has no idea if the parameters make sense or not and will happily converge with models that are overparameterised. You cannot rely on a failed $COV step or a MINIMIZATION TERMINATED message to conclude the model is not a good one. You need to use your brains (NONMEM does not have a brain) and your common sense to decide if your model makes sense or is perhaps overparameterised. Nick Ethan Wu wrote: > > Dear all, > > I am fitting a PD response, and the equation goes like this: > > total response = baseline+f(placebo response) +f(drug response) > > first, I tried full omega block, and model was able to converge, but > $COV stop failed. > > To me, this indicates that too many parameters in the model. The > structure model is rather simple one, so I think probably too many Etas. > > I wonder is there a good principle of Eta reduction that I could > implement here. Any good reference? > > -- Nick Holford, Dept Pharmacology & Clinical Pharmacology University of Auckland, 85 Park Rd, Private Bag 92019, Auckland, New Zealand n.holf...@auckland.ac.nz tel:+64(9)923-6730 fax:+64(9)373-7090 mobile: +33 64 271-6369 (Apr 6-Jul 17 2009) http://www.fmhs.auckland.ac.nz/sms/pharmacology/holford