Skip to Main content Skip to Navigation
Journal articles

Performance in population models for count data, part II: a new SAEM algorithm.

Radojka Savic 1, * Marc Lavielle 2
* Corresponding author
2 SELECT - Model selection in statistical learning
LMO - Laboratoire de Mathématiques d'Orsay, Inria Saclay - Ile de France
Abstract : Analysis of count data from clinical trials using mixed effect analysis has recently become widely used. However, algorithms available for the parameter estimation, including LAPLACE and Gaussian quadrature (GQ), are associated with certain limitations, including bias in parameter estimates and the long analysis runtime. The stochastic approximation expectation maximization (SAEM) algorithm has proven to be a very efficient and powerful tool in the analysis of continuous data. The aim of this study was to implement and investigate the performance of a new SAEM algorithm for application to count data. A new SAEM algorithm was implemented in MATLAB for estimation of both, parameters and the Fisher information matrix. Stochastic Monte Carlo simulations followed by re-estimation were performed according to scenarios used in previous studies (part I) to investigate properties of alternative algorithms (Plan et al., 2008, Abstr 1372 [ http://wwwpage-meetingorg/?abstract=1372 ]). A single scenario was used to explore six probability distribution models. For parameter estimation, the relative bias was less than 0.92% and 4.13% for fixed and random effects, for all models studied including ones accounting for over- or under-dispersion. Empirical and estimated relative standard errors were similar, with distance between them being <1.7% for all explored scenarios. The longest CPU time was 95 s for parameter estimation and 56 s for SE estimation. The SAEM algorithm was extended for analysis of count data. It provides accurate estimates of both, parameters and standard errors. The estimation is significantly faster compared to LAPLACE and GQ. The algorithm is implemented in Monolix 3.1, (beta-version available in July 2009).
Complete list of metadatas

Cited literature [6 references]  Display  Hide  Download

https://www.hal.inserm.fr/inserm-00470358
Contributor : Emmanuelle Comets <>
Submitted on : Tuesday, April 6, 2010 - 1:02:34 PM
Last modification on : Wednesday, September 16, 2020 - 5:06:39 PM
Long-term archiving on: : Tuesday, September 14, 2010 - 4:58:01 PM

Files

Savic_JPKPD_2009.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

Radojka Savic, Marc Lavielle. Performance in population models for count data, part II: a new SAEM algorithm.. Journal of Pharmacokinetics and Pharmacodynamics, Springer Verlag, 2009, 36 (4), pp.367-79. ⟨10.1007/s10928-009-9127-7⟩. ⟨inserm-00470358⟩

Share

Metrics

Record views

390

Files downloads

1452