Nonparametric goodness-of-fit testing for parametric covariate models in pharmacometric analyses.
CPT Pharmacometrics Syst Pharmacol
; 10(6): 564-576, 2021 06.
Article
en En
| MEDLINE
| ID: mdl-33755347
The characterization of covariate effects on model parameters is a crucial step during pharmacokinetic/pharmacodynamic analyses. Although covariate selection criteria have been studied extensively, the choice of the functional relationship between covariates and parameters, however, has received much less attention. Often, a simple particular class of covariate-to-parameter relationships (linear, exponential, etc.) is chosen ad hoc or based on domain knowledge, and a statistical evaluation is limited to the comparison of a small number of such classes. Goodness-of-fit testing against a nonparametric alternative provides a more rigorous approach to covariate model evaluation, but no such test has been proposed so far. In this manuscript, we derive and evaluate nonparametric goodness-of-fit tests for parametric covariate models, the null hypothesis, against a kernelized Tikhonov regularized alternative, transferring concepts from statistical learning to the pharmacological setting. The approach is evaluated in a simulation study on the estimation of the age-dependent maturation effect on the clearance of a monoclonal antibody. Scenarios of varying data sparsity and residual error are considered. The goodness-of-fit test correctly identified misspecified parametric models with high power for relevant scenarios. The case study provides proof-of-concept of the feasibility of the proposed approach, which is envisioned to be beneficial for applications that lack well-founded covariate models.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Modelos Estadísticos
/
Modelos Biológicos
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Child
/
Humans
Idioma:
En
Revista:
CPT Pharmacometrics Syst Pharmacol
Año:
2021
Tipo del documento:
Article
País de afiliación:
Alemania
Pais de publicación:
Estados Unidos