Search: id:"swepub:oai:DiVA.org:uu-122145" >
Performance in popu...
Performance in population models for count data, part I: maximum likelihood approximations.
-
- Plan, Elodie L. (author)
- Uppsala universitet,Institutionen för farmaceutisk biovetenskap,Farmakometri
-
- Maloney, Alan (author)
- Uppsala universitet,Institutionen för farmaceutisk biovetenskap,Farmakometri
-
- Trocóniz, Iñaki F. (author)
- Uppsala universitet,Institutionen för farmaceutisk biovetenskap,Farmakometri
-
show more...
-
- Karlsson, Mats O. (author)
- Uppsala universitet,Institutionen för farmaceutisk biovetenskap,Farmakometri
-
show less...
-
(creator_code:org_t)
- 2009-08-04
- 2009
- English.
-
In: Journal of Pharmacokinetics and Pharmacodynamics. - : Springer Science and Business Media LLC. - 1567-567X .- 1573-8744. ; 36:4, s. 353-366
- Related links:
-
https://urn.kb.se/re...
-
show more...
-
https://doi.org/10.1...
-
show less...
Abstract
Subject headings
Close
- There has been little evaluation of maximum likelihood approximation methods for non-linear mixed effects modelling of count data. The aim of this study was to explore the estimation accuracy of population parameters from six count models, using two different methods and programs. Simulations of 100 data sets were performed in NONMEM for each probability distribution with parameter values derived from a real case study on 551 epileptic patients. Models investigated were: Poisson (PS), Poisson with Markov elements (PMAK), Poisson with a mixture distribution for individual observations (PMIX), Zero Inflated Poisson (ZIP), Generalized Poisson (GP) and Negative Binomial (NB). Estimations of simulated datasets were completed with Laplacian approximation (LAPLACE) in NONMEM and LAPLACE/Gaussian Quadrature (GQ) in SAS. With LAPLACE, the average absolute value of the bias (AVB) in all models was 1.02% for fixed effects, and ranged 0.32-8.24% for the estimation of the random effect of the mean count (lambda). The random effect of the overdispersion parameter present in ZIP, GP and NB was underestimated (-25.87, -15.73 and -21.93% of relative bias, respectively). Analysis with GQ 9 points resulted in an improvement in these parameters (3.80% average AVB). Methods implemented in SAS had a lower fraction of successful minimizations, and GQ 9 points was considerably slower than 1 point. Simulations showed that parameter estimates, even when biased, resulted in data that were only marginally different from data simulated from the true model. Thus all methods investigated appear to provide useful results for the investigated count data models.
Subject headings
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinska och farmaceutiska grundvetenskaper -- Farmaceutiska vetenskaper (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Basic Medicine -- Pharmaceutical Sciences (hsv//eng)
Keyword
- NONMEM
- LAPLACE
- Laplacian approximation
- SAS
- Gaussian quadrature
- Maximum likelihood approximation
- Count data
- Poisson model
- PHARMACY
- FARMACI
Publication and Content Type
- ref (subject category)
- art (subject category)
Find in a library
To the university's database