Sökning: onr:"swepub:oai:DiVA.org:uu-216136" >
Evaluation of Bias,...
Evaluation of Bias, Precision, Robustness and Runtime for Estimation Methods in NONMEM 7
-
- Johansson, Åsa M., 1983- (författare)
- Uppsala universitet,Institutionen för farmaceutisk biovetenskap,Pharmacometrics Research Group
-
- Ueckert, Sebastian, 1983- (författare)
- Uppsala universitet,Institutionen för farmaceutisk biovetenskap,Pharmacometrics Research Group
-
- Plan, Elodie L., 1981- (författare)
- Uppsala universitet,Institutionen för farmaceutisk biovetenskap,Pharmacometrics Research Group
-
visa fler...
-
- Hooker, Andrew C. (författare)
- Uppsala universitet,Institutionen för farmaceutisk biovetenskap,Pharmacometrics Research Group
-
- Karlsson, Mats O. (författare)
- Uppsala universitet,Institutionen för farmaceutisk biovetenskap,Pharmacometrics Research Group
-
visa färre...
-
(creator_code:org_t)
- 2014-05-07
- 2014
- Engelska.
-
Ingår i: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 41:3, s. 223-238
- Relaterad länk:
-
https://urn.kb.se/re...
-
visa fler...
-
https://doi.org/10.1...
-
visa färre...
Abstract
Ämnesord
Stäng
- NONMEM is the most widely used software for population pharmacokinetic (PK)-pharmacodynamic (PD) analyses. The latest version, NONMEM 7 (NM7), includes several sampling-based estimation algorithms in addition to the classical algorithms. In this study, performance of the estimation algorithms available in NM7 was investigated with respect to bias, precision, robustness and runtime for a diverse set of PD models. Simulations of 500 data sets from each PD model were reanalyzed with the available estimation algorithms to investigate bias and precision. Simulations of 100 data sets were used to investigate robustness by comparing final estimates obtained after estimations starting from the true parameter values and initial estimates randomly generated using the CHAIN feature in NM7. Average estimation time for each algorithm and each model was calculated from the runtimes reported by NM7.The algorithm giving the lowest bias and highest precision across models was importance sampling (IMP), closely followed by FOCE/LAPLACE and stochastic approximation expectation-maximization (SAEM). The algorithms relative robustness differed between models, but FOCE/LAPLACE was the most robust algorithm across models, followed by SAEM and IMP. FOCE/LAPLACE was also the algorithm with the shortest runtime for all models, followed by iterative two-stage (ITS). The Bayesian Markov Chain Monte Carlo method, used in this study for point estimation, performed worst in all tested metrics.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinska och farmaceutiska grundvetenskaper -- Farmaceutiska vetenskaper (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Basic Medicine -- Pharmaceutical Sciences (hsv//eng)
Nyckelord
- NONMEM
- estimation algorithms
- Pharmaceutical Science
- Farmaceutisk vetenskap
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
Hitta via bibliotek
Till lärosätets databas