TY  - JOUR
A1  - Hartung, Niklas
A1  - Wahl, Martin
A1  - Rastogi, Abhishake
A1  - Huisinga, Wilhelm
T1  - Nonparametric goodness-of-fit testing for parametric covariate models in pharmacometric analyses
JF  - CPT: pharmacometrics & systems pharmacology
N2  - 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.
Y1  - 2021
U6  - https://doi.org/10.1002/psp4.12614
SN  - 2163-8306
VL  - 10
IS  - 6
SP  - 564
EP  - 576
PB  - Nature Publ. Group
CY  - London
ER  -