Open AccessBenchmarking QSP Models Against Simple Models: A Path to Improved Comprehension and Predictive Performance
Author(s) -
Stein Andrew M.,
Looby Michael
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/psp4.12311
Subject(s) - benchmarking , computer science , simple (philosophy) , path (computing) , machine learning , artificial intelligence , programming language , philosophy , epistemology , marketing , business
Quantitative Systems Pharmacology (QSP) models provide a means of integrating knowledge into a quantitative framework and, ideally, this integration leads to a better understanding of biology and better predictions of new experiments and clinical trials. In practice, these goals may be compromised by model complexity and uncertainty. To address these problems, we recommend that the predictive performance of QSP models be assessed through comparison with simpler models developed specifically for this purpose.