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.
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- clinical chemistry
- basic science