Biostatistics for Bioassay
Author:   Ann Yellowlees ,  Matthew Stephenson
Publisher:   Taylor & Francis Ltd
ISBN:  

9781032582429


Pages:   296
Publication Date:   26 January 2026
Format:   Paperback
Availability:   Not yet available   Availability explained
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Biostatistics for Bioassay


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Full Product Details

Author:   Ann Yellowlees ,  Matthew Stephenson
Publisher:   Taylor & Francis Ltd
Imprint:   Chapman & Hall/CRC
Weight:   0.453kg
ISBN:  

9781032582429


ISBN 10:   1032582421
Pages:   296
Publication Date:   26 January 2026
Audience:   College/higher education ,  Professional and scholarly ,  Tertiary & Higher Education ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Forthcoming
Availability:   Not yet available   Availability explained
This item is yet to be released. You can pre-order this item and we will dispatch it to you upon its release.

Table of Contents

I Potency and its measurement: bioassays and their performance. 1. Introduction - bioassays and potency. 2. Bioassay performance requirements. 3. Bioassay design. II Estimation of potency: the statistical analysis of bioassay runs. 4. Statistical models for characterising the dose-response relationship. 5. Choosing the dose-response relationship for bioassay data. 6. Assay data analysis. 7. Bioassay suitability criteria. III Validation, monitoring and modifications. 8. Qualification and validation of bioassays. 9. Monitoring the performance of a bioassay procedure.10.Bioassay updates and modifications.

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Author Information

Ann Yellowlees is Founder of Quantics Biostatistics. Ann has worked in academia, Shell Research, and the NHS in Scotland. She has been consulting in bioassay statistics since 2006. She holds degrees in both mathematics and applied statistics from the University of Oxford, and a PhD in statistics from the University of Waterloo. Matthew Stephenson is a Director of Statistics at Quantics Biostatistics, with an undergraduate degree in human kinetics, and an MSc and PhD in statistics from the University of Guelph. He won the 2020 Canadian Journal of Statistics Award for his PhD research on leveraging graphical structure among predictors to improve outcome prediction.

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