PRIOR method without covariance matrix of estimates?
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 Kner
PK/PD Sciences
Modelling and Simulation (RDP/MS)
Nycomed: A Takeda Company
Nycomed GmbH
Byk-Gulden-Str. 2
D-78467 Konstanz, Germany
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mailto: nele.kaessner
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