Re: Successful minimization and covariance

From: Ayyappa Chaturvedula [email protected] Date: Thu, 24 May 2012 12:27:03 -0400 To: [email protected] Subject: Re: [NMusers] Successful minimization and covariance Thank you for the suggestions. I will work on those. It is a orally administered drug. We do have good spread of the data points to support a 2-com model and prior knowledge on the drug supports this. On May 24, 2012, at 12:21 PM, "[email protected]" <[email protected]> wrote: > Ayyappa, > Since you already have bootstrap data, you may check few things: > 1. Take any of the runs which has successful covariance step, and look on > the > RSEs. Are there any exceeding, say 60-70%? If yes, these parameters are not > supported by the data. If all RSEs looks good, this would support validity > of > the model > 2. Compute eigenvalues of the correlation matrix of the bootstrap parameter > estimates. Roughly, the values above 1000 (for the ratio of the maximum to > minimum of these eigenvalues) may indicate over-parameterization. > 3. Create a scatter-plot matrix of bootstrap parameter estimates (N by N > matrix > of plots where plot i-j is the 1000 parameter-i values plotted against 1000 > parameter-j values, or some variant of this diagnostics). If any of the > parameters are strongly correlated, you will immediately see it on these > plots. > > If none of these diagnostics reveals any suspicious behavior, I would > accept the > model as is. > > Another place to look is outliers. Few points with unrealistic > concentrations > may lead to minimization or COV failure. In your case, this is not likely > since > many bootstrap runs do not converge. > > Yes another place is to look on your matrix of random effects: too many > random > effects may lead to non-convergences if these effects are not supported by > the > data. > > Do you have oral, IV or mixed dosing? Was it done in differential equations > (ADVAN 6, 8, 9, 13) or as exact solution (ADVAN 3-4)? Were there any > covaraite > effect in the model, and if yes, was there a sufficient range of data to > support > these effects? > > Leonid > > > > Original Message: > ----------------- > From: Ayyappa Chaturvedula [email protected] > Date: Thu, 24 May 2012 08:33:21 -0400 > To: [email protected] > Subject: [NMusers] Successful minimization and covariance > > > Dear Group, > This is a topic that has been discussed and different schools of thinking > exist > to my knowledge. But, I want to restate my case and get some opinions. > The > question is about how important to have successful minimization and > covariance > if diagnostics make sense. I have developed a two compartment model with > a > Phase 3 trial data and minimization was successful but covariance step was > not. > I went ahead and did a 1000 run bootstrap and wanted to get the confidence > intervals of parameters. There are 60% runs that are not successfully > minimized > and many other do not have covariance step successful. I put together CI > from > the runs that have successful minimization and also including all 1000 > runs. > There is no difference in the parameter estimate or the confidence interval > (less than 5% change in numbers). The model diagnostics look good > including > VPC, NPDE plots, basic gof and a simulation to explain another trial data. > Now, > my question is in this particular case do I have to worry further to make > the > successful covariance step and increase the number of runs that gets > successfully minimized in the bootstrap even though I cannot see much > difference in the parameter estimates, diagnostics? My bottom line is not > going > to change in anyway. I appreciate your expert opinions. > > Regards, > Ayyappa > > -------------------------------------------------------------------- > mail2web LIVE – Free email based on Microsoft® Exchange technology - > http://link.mail2web.com/LIVE > > -------------------------------------------------------------------- mail2web.com - Microsoft® Exchange solutions from a leading provider - http://link.mail2web.com/Business/Exchange