Open AccessA Regression Approach to Visual Predictive Checks for Population Pharmacometric Models
Author(s) -
Jamsen Kris M.,
Patel Kashyap,
Nieforth Keith,
Kirkpatrick Carl M. J.
Publication year - 2018
Publication title -
cpt: pharmacometrics and systems pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.53
H-Index - 37
ISSN - 2163-8306
DOI - 10.1002/psp4.12319
Subject(s) - computer science , regression , population , regression analysis , quantile regression , logistic regression , statistics , data mining , econometrics , machine learning , mathematics , medicine , environmental health
A visual predictive check ( VPC ) is a common diagnostic procedure for population pharmacometric models. Typically, VPC s are generated by specifying intervals, or “bins”, of an independent variable (e.g., time). However, bin specification is not always straightforward and the choice of bins may affect the appearance, and possibly conclusions, of VPC s. The objective of this work was to demonstrate how regression techniques can be used to derive VPC s and prediction‐corrected VPC s (pc VPC s) for population pharmacometric models. This alternative approach negates the need for empirical bin selection. The proposed method utilizes local and additive quantile regression. Implementation is straightforward and computationally acceptable. This work provides support for deriving VPC s and pc VPC s via regression techniques.