Approaches to nonlinear curve fitting in laboratory medicine.

basic science chemistry clinical chemistry immunology informatics toxicology

Journal

Laboratory medicine
ISSN: 1943-7730
Titre abrégé: Lab Med
Pays: England
ID NLM: 0250641

Informations de publication

Date de publication:
01 Aug 2023
Historique:
medline: 1 8 2023
pubmed: 1 8 2023
entrez: 1 8 2023
Statut: aheadofprint

Résumé

Nonlinear curve fitting is an important process in laboratory medicine, particularly with the increased use of highly sensitive antibody-based assays. Although the process is often automated in commercially available software, it is important that clinical scientists and physicians recognize the limitations of the various approaches used and are able to select the most appropriate model. This article summarizes the key nonlinear functions and demonstrates their application to common laboratory data. Following this, a basic overview of the statistical comparison of models is presented and then a discussion of important algorithms used in nonlinear curve fitting. An accompanying Microsoft Excel workbook is available that can be used to explore the content of this article.

Identifiants

pubmed: 37527550
pii: 7234994
doi: 10.1093/labmed/lmad069
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© The Author(s) 2023. Published by Oxford University Press on behalf of American Society for Clinical Pathology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Auteurs

Peter A C McPherson (PAC)

Ulster University, School of Pharmacy & Pharmaceutical Science, Coleraine, UK.

Classifications MeSH