Re: PopED and SSE comparison

On 10/8/2013 10:50 AM, pavan kumar wrote: > Hi, > I have been working on a fairly complex differential equation based > model with an objective to optimize study design particularly for number > of subjects in an experiment. The PK model is a lme model between dose > and AUC and the PKPD model consists of a placebo component and a drug > effect component with fixed PK model parameters from the PK model (both > developed in NONMEM). Design optimization is run using PopED. > My interest lies particularly in the drug effect parameters of the model > (Emax and EAUC50). I have log transformed parameters as part of the MU > model (I am using SAEM, in NM7.2) and I calculated NONMEM %RSEs for > the untransformed parameters as SE(log_transformed)*100, which were > around 11 and 25 %RSE. > When the same design was set up in PopED and evaluated using FO and > reduced FIM option, the precision of the parameters from the expected > FIM (calculated as sqrt(expected parameter variances) * 100) were over > predicted for the drug effect parameters particularly EAUC50 (~18% and > 75%). > When I ran an SSE (N=200, given the complexity of the model and the long > run times associated with it, inspite of using parallelization) with the > original design, the %RSE (calculated as the sd *100/sqrt(200) from the > sse_results.csv), showed much smaller imprecision smaller than what > NONMEM provided (< 2%RSE). I evaluated the precision for other designs > using PopED and for a few of those designs ran the SSE as well. I have a > similar observation that the PopED precisions were much larger than the > SSE runs. > I have the following questions: > 1. Am I missing something in the calculation of %RSE involving log > transformed parameters that I am seeing such odd results from the three > approaches? Is there a better way to compare these results across these > approaches in such a case of log transformed parameters (eg. using CI of > the log transformed parameters)? > 2. Does estimation method (SAEM in NONMEM vs FO/FOCE in PopED) play a > role in such differences? > 3. Should SSE be considered gold standard? How should I interpret the > results if I see a bias in the model parameters from SSE? > 4. As you are aware, we can fix some of the parameters in PopED and do > an evaluation. To compare such results with SSE, should I fix the same > parameters that were fixed in PopED and run an SSE? > I would like to hear your thoughts on what is the best way to identify a > future design in such a situation? I appreciate your timely help! > Thanks, > Pavan.