An Introduction to Statistics for Bioassay (June 2025)

Our bioassay statistics training course is designed to give an overview of the statistics behind your bioassay. Our expert statisticians lead the way, using their years of experience to highlight useful tips and tricks for your next bioassay.

We don’t assume any prior knowledge of biostatistics, so the course is perfect for any background, whether you’re working in the lab, in QA, or beyond!

If you have any questions, please don’t hesitate to get in touch with our Training Coordinator, Jason: jason.segall@quantics.co.uk

*PLEASE NOTE*

If you wish to attend both of the two-day sessions, please select BOTH DAY ONE AND DAY TWO at checkout.

Price includes VAT. A detailed schedule for the event will be sent to attendees in advance of the course.

COURSE OUTLINE:

This flexible course covers the fundamental elements of bioassay statistics. It explores the basics of analysis and assay development, through to assay optimisation, validation and long term management in routine use. The course will provide a solid background knowledge of the statistical methodology used sufficient to understand and resolve issues that can cause problems with real world data analysis.

Day 1

Module 1: Relative Potency

This module starts with an explanation of what relative potency is, and why it has become accepted by both the industry and regulators as the standard way of reporting potency. The basics of how relative potency is calculated are covered, as well as an introduction to the concept of parallelism. We also take a brief tour of the regulatory guidance for bioassay, which underpins the majority of the training.

Module 2: Statistical Models

Next we introduce the concept of continuous and binary data in bioassay, and examine different ways these are modelled. In the former case, we discuss linear, slope ratio, 4PL, and 5PL models, and, for the latter, we examine logit and probit models. We demonstrate how relative potency is calculated for all covered models.

We then discuss some of the nuances of modelling bioassay data, covering model fitting, variance homogeneity, and response transforms in detail.

Module 3: Parallelism

In this module, we take a deep dive into the concept of parallelism. We examine why it is so vital for calculating relative potency, and look at how testing for parallelism is performed. In particular, we discuss significance testing–with particular focus on the F Test–and equivalence testing, before examining the pros and cons of both methods.

Module 4: Suitability Criteria

Here, we introduce system and sample suitability criteria. These, respectively, check the assay is performing as expected, and that our samples are behaving as expected. Several examples of suitability criteria are discussed, including goodness-of-fit and precision factor, along with testing methods. Finally, we provide some tips and tricks for using these concepts in real bioassays.

Module 5: Outliers

This module examines the controversial topic of outliers. We discuss methods of detecting statistical outliers, such as Grubbs’ Test, as well as modelling methods which can accommodate outliers, such as robust regression. We also highlight some of the pitfalls of outlier removal, before concluding with a summary of Day 1 through selected examples.

Day 2

Module 6: Validation I

In this module, we tackle the theory behind bioassay validation, which is used to prove an assay meets its suitability criteria to regulators. We discuss validation of accuracy, precision, and range, as well as introducing the basics of the design of a validation study.

Module 7: Validation II

We follow on from the theory of validation with a demonstration of applying these ideas to a real dataset. We perform calculations to find an appropriate number of runs for the validation, before performing that validation for the accuracy and precision for our example data. We take a brief look at the varaince components of our dataset, before concluding with an assessment of the assay range and suitability criteria.

Module 8: Assay Optimisation

Here, we introduce assay leaning, which aims to make assay design more efficient without reducing precision. Several optimisation methods are discussed, including dose and replicate reduction and suitability criterion choice. We also take a brief look at variance components analysis, as well as examining the pros and cons of leaning at different points of the bioassay life cycle.

Module 9: Assay Monitoring

This module describes the process of continued monitoring of bioassays to check their performance through routine use. We discuss how to choose which endpoints to monitor, and what rules should be imposed on incoming data to ensure the assay is continuing to behave as expected. Finally, we provide recommendations for avoiding common traps in setting up assay monitoring.

Module 10: Tech Transfer & Reference Bridging

Our final module covers the statistics behind avoiding re-validation when an assay is transferred to a new site, including setting acceptance criteria on comparability and variability. We then examine reference bridging, with the reasons behind changing references discussed, along with ways to measure the behaviour of a bioassay with a new reference standard.