Statistical Bioassay

Theory, Design, and Computation

Learn to estimate and interpret relative potency, assess assay validity and choose an experimental design. This graduate textbook develops direct, parallel-line, slope-ratio and quantal assays, with worked examples in R and Python.

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12 chapters · 168 body pages · R and Python · Worked examples and exercises

Before you start

Probability, statistical inference and basic regression. Familiarity with either R or Python will help with the computational examples.

Read the preface and contents.

Your study route

Foundations and potency estimation

  1. 01Introduction to Biological Assay
  2. 02Statistical and Probabilistic Foundations for Bioassay
  3. 03Fieller’s Theorem and Fiducial Limits
  4. 04Direct Assays
  5. 05The Behrens–Fisher–Sukhatme Distribution

Assay models and validity

  1. 06Parallel-Line Assays
  2. 07Slope-Ratio Assays
  3. 08Quantal Response Assays — Probit and Logit Analysis
  4. 09Validity Tests, the Condition of Similarity, and Variance Homogeneity

Experimental design and case studies

  1. 10Design of Experiments in Bioassay — Block Designs
  2. 11Cross-Over Designs and Washout Periods
  3. 12Comprehensive Case Studies

Solutions and reference materials

How to use the course

Follow the chapters in sequence, or use the sidebar to revisit a method. Each reading has page controls, text search and a downloadable PDF. Attempt the exercises independently before opening the answer key. Keep a record of the assumptions, computations and interpretation for each worked example.

NoteAbout this teaching edition

The chapter readers contain the complete supplied textbook pages. The website adds navigation and study guidance; the original mathematical notation, code, diagrams, exercises and solutions are preserved. The full download combines the supplied preliminary material and body into one bookmarked textbook.

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