Sökning: WFRF:(Bjugård Nyberg Henrik) >
Saddle-Reset for Ro...
Saddle-Reset for Robust Parameter Estimation and Identifiability Analysis of Nonlinear Mixed Effects Models
-
- Bjugård Nyberg, Henrik (författare)
- Uppsala universitet,Institutionen för farmaceutisk biovetenskap
-
- Hooker, Andrew C., 1973- (författare)
- Uppsala universitet,Institutionen för farmaceutisk biovetenskap
-
- Bauer, Robert J (författare)
- Pharmacometrics R&D, ICON CLINICAL RESEARCH LLC, Gaithersburg, Maryland, USA
-
visa fler...
-
- Aoki, Yasunori, 1982- (författare)
- Uppsala universitet,Institutionen för farmaceutisk biovetenskap
-
visa färre...
-
(creator_code:org_t)
- 2020-07-02
- 2020
- Engelska.
-
Ingår i: AAPS Journal. - : Springer Science and Business Media LLC. - 1550-7416. ; 22:4
- Relaterad länk:
-
https://doi.org/10.1...
-
visa fler...
-
https://uu.diva-port... (primary) (Raw object)
-
https://link.springe...
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
visa färre...
Abstract
Ämnesord
Stäng
- Parameter estimation of a nonlinear model based on maximizing the likelihood using gradient-based numerical optimization methods can often fail due to premature termination of the optimization algorithm. One reason for such failure is that these numerical optimization methods cannot distinguish between the minimum, maximum, and a saddle point; hence, the parameters found by these optimization algorithms can possibly be in any of these three stationary points on the likelihood surface. We have found that for maximization of the likelihood for nonlinear mixed effects models used in pharmaceutical development, the optimization algorithm Broyden-Fletcher-Goldfarb-Shanno (BFGS) often terminates in saddle points, and we propose an algorithm, saddle-reset, to avoid the termination at saddle points, based on the second partial derivative test. In this algorithm, we use the approximated Hessian matrix at the point where BFGS terminates, perturb the point in the direction of the eigenvector associated with the lowest eigenvalue, and restart the BFGS algorithm. We have implemented this algorithm in industry standard software for nonlinear mixed effects modeling (NONMEM, version 7.4 and up) and showed that it can be used to avoid termination of parameter estimation at saddle points, as well as unveil practical parameter non-identifiability. We demonstrate this using four published pharmacometric models and two models specifically designed to be practically non-identifiable.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinska och farmaceutiska grundvetenskaper -- Farmaceutiska vetenskaper (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Basic Medicine -- Pharmaceutical Sciences (hsv//eng)
Nyckelord
- NLME
- estimation methods
- parameter estimation
- pharmacometrics
- practical identifiability
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
Hitta via bibliotek
Till lärosätets databas