@article{HartungWahlRastogietal.2021,
  author    = {Hartung, Niklas and Wahl, Martin and Rastogi, Abhishake and Huisinga, Wilhelm},
  title     = {Nonparametric goodness-of-fit testing for parametric covariate models in pharmacometric analyses},
  series = {CPT: pharmacometrics \& systems pharmacology},
  volume    = {10},
  journal   = {CPT: pharmacometrics \& systems pharmacology},
  number    = {6},
  publisher = {Nature Publ. Group},
  address   = {London},
  issn      = {2163-8306},
  doi       = {10.1002/psp4.12614},
  pages     = {564 -- 576},
  year      = {2021},
  abstract  = {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.},
  language  = {en}
}