ELISA validation in the pharmaceutical industry: a mixed-effects models approach with R
In the first part of this work, a methodology to gain insight into ELISA performance and finally obtain accuracy and precision estimates using linear mixed effects models is presented. Also, in the second part, non-linear mixed effects models are applied as a tool to establish parallelism between te...
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| Format: | master thesis |
| Publication Date: | 2018 |
| Country: | España |
| Institution: | Universitat Oberta de Catalunya (UOC) |
| Repository: | O2, repositorio institucional de la UOC |
| OAI Identifier: | oai:openaccess.uoc.edu:10609/91346 |
| Online Access: | http://hdl.handle.net/10609/91346 |
| Access Level: | Open access |
| Keyword: | mixed effects models ELISA models d'efectes mixtes modelos de efectos mixtos Bioinformatics -- TFM Bioinformàtica -- TFM Bioinformática -- TFM |
| Summary: | In the first part of this work, a methodology to gain insight into ELISA performance and finally obtain accuracy and precision estimates using linear mixed effects models is presented. Also, in the second part, non-linear mixed effects models are applied as a tool to establish parallelism between test and reference product preparations when conducting full dose-response assays. Overall, both linear and non-linear mixed effects have demonstrated to be complex yet extremely powerful and versatile tools. Great knowledge on the impact of potential nuisance factors has been extracted using the workflows presented and more precise estimates of important assay parameters have been established. The work has been developed in R statistical programming language which contributes to the potential of the framework itself. |
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