Re: Successful minimization and covariance
Ayyappa,
You have confirmed what several others have also found - NONMEM bootstrap estimates of parameter confidence intervals are not sensitive to the NONMEM termination state (especially whether or not the covariance step is successful). This result is of course only an empirical one and not based on any theory that allows one to draw general conclusions. But until someone comes up with a clear counterexample (and hopefully with an explanation of the circumstances) then I think you can pragmatically accept the bootstrap confidence intervals and get on with more interesting things :-)
Nick
Quoted reply history
On 24/05/2012 2:33 p.m., Ayyappa Chaturvedula wrote:
> 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
--
Nick Holford, Professor Clinical Pharmacology
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