Re: PRIOR method without covariance matrix of estimates?
Hi Norman,
Priors is the summary of the data. You always loose something when you summarize the data. Sometime you have to use summaries because data are not available or when you want to support some parts of the model while allowing more flexibility to the other parts (for example, when you use adult priors + allometric scaling to support pediatric model). In other cases, it is better to use raw data without intermediate steps.
One (artificial but still valid) example is the nonlinear drug. At each dose level, you could be able to describe it by the linear model. But if you have data from several dose levels, you can develop the nonlinear model. Now assume that you have several (say 3) separate studies, each at one dose level. You will have 3 different linear models, each with different parameters, and with some uncertainty. I am not sure how to get a meaningful model out of these data summaries. Similarly, if you have different populations, or different distribution/representation of covariates, or different sampling scheme - I think it would be more beneficial to have all these diverse data in one set that to transfer information through the parameters.
Still, each particular case should be approached individually, I would not rule out any of the approaches (priors vs combining the data) without knowing details of the specific case.
Thanks
Leonid
--------------------------------------
Leonid Gibiansky, Ph.D.
President, QuantPharm LLC
web: www.quantpharm.com
e-mail: LGibiansky at quantpharm.com
tel: (301) 767 5566
Quoted reply history
On 10/14/2011 10:45 PM, Norman Z wrote:
> Hi Leonid,
> Would you please explain why combine the data may be a better option
> than using priors? I've met similar situations.
> Thanks,
> Norman
>
> On Fri, Oct 14, 2011 at 5:01 PM, Leonid Gibiansky
> <[email protected] <mailto:[email protected]>> wrote:
>
> Nele,
>
> Nonmem 7.2 has UNCONDITIONAL option in cov step that would compute
> SEs even for problems with error in minimization.
>
> Also, it is perfectly OK to have variance-covariance computed from
> bootstrap; some may even argue that it is better than use Nonmem cov
> matrix.
>
> And finally (just an opinion) I would rather combine the data than
> use priors.
>
> Thanks
> Leonid
>
> ------------------------------__--------
> Leonid Gibiansky, Ph.D.
> President, QuantPharm LLC
> web: www.quantpharm.com http://www.quantpharm.com/
> e-mail: LGibiansky at quantpharm.com http://quantpharm.com/
> tel: (301) 767 5566 <tel:%28301%29%20767%205566>
>
> On 10/13/2011 1:31 AM, Kaessner, Nele wrote:
>
> Dear all,
>
> I am currently in a planning phase for a PopPK evaluation for an
> upcoming study. I have already developed a PopPK model for this
> compound, and the idea would now be to fit the available model
> to the
> new data. One idea was to do this via the PRIOR functionality in
> NONMEM,
> so that I do not have to mix the old data into the new data set.
> So now
> I wanted to test the performance of this approach via
> simulations, and
> have the following problem:
>
> My previously developed final model did not converge successfully
> (terminated with rounding errors), so I do not have a covariance
> matrix
> of estimates to describe the uncertainty in the THETAs. However I
> obtained confidence intervals from a nonparametric bootstrap,
> and I was
> wondering if these results can somehow be used instead for the prior
> information.
>
> In some internal discussions we had the idea to just use all the
> parameter estimates from the performed 1000 bootstrap runs, and just
> calculate variance and covariance of THETAs from these.
>
> I would highly appreciate your comments on the validity of such an
> approach, or suggestions of other alternatives.
>
> Thanks and best regards
>
> Nele
>
> __________________________________________________________________
>
> Dr. Nele Kنكner
>
> PK/PD Sciences
>
> Modelling and Simulation (RDP/MS)
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