Open AccessConfidence and Prediction Intervals for Pharmacometric Models
Author(s) -
Kümmel Anne,
Bonate Peter L.,
Dingemanse Jasper,
Krause Andreas
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/psp4.12286
Subject(s) - confidence interval , prediction interval , key (lock) , predictive modelling , medicine , drug development , computer science , statistics , econometrics , drug , data mining , machine learning , pharmacology , mathematics , computer security
Supporting decision making in drug development is a key purpose of pharmacometric models. Pharmacokinetic models predict exposures under alternative posologies or in different populations. Pharmacodynamic models predict drug effects based on exposure to drug, disease, or other patient characteristics. Estimation uncertainty is commonly reported for model parameters; however, prediction uncertainty is the key quantity for clinical decision making. This tutorial reviews confidence and prediction intervals with associated calculation methods, encouraging pharmacometricians to report these routinely.