Efficient algorithms for optimal designs with correlated observations in pharmacokinetics and dose-finding studies.
Biometrics
; 68(1): 138-45, 2012 Mar.
Article
en En
| MEDLINE
| ID: mdl-21838804
ABSTRACT
Random effects models are widely used in population pharmacokinetics and dose-finding studies. However, when more than one observation is taken per patient, the presence of correlated observations (due to shared random effects and possibly residual serial correlation) usually makes the explicit determination of optimal designs difficult. In this article, we introduce a class of multiplicative algorithms to be able to handle correlated data and thus allow numerical calculation of optimal experimental designs in such situations. In particular, we demonstrate its application in a concrete example of a crossover dose-finding trial, as well as in a typical population pharmacokinetics example. Additionally, we derive a lower bound for the efficiency of any given design in this context, which allows us on the one hand to monitor the progress of the algorithm, and on the other hand to investigate the efficiency of a given design without knowing the optimal one. Finally, we extend the methodology such that it can be used to determine optimal designs if there exist some requirements regarding the minimal number of treatments for several (in some cases all) experimental conditions.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Farmacocinética
/
Interpretación Estadística de Datos
/
Biometría
/
Estudios Cruzados
/
Relación Dosis-Respuesta a Droga
Tipo de estudio:
Clinical_trials
/
Diagnostic_studies
/
Prognostic_studies
Idioma:
En
Revista:
Biometrics
Año:
2012
Tipo del documento:
Article
País de afiliación:
Alemania