Reproducibility in Biomedical Research: Epistemological and Statistical Problems, Second Edition explores the ideas and conundrums inherent in scientific research. This second edition addresses new challenges to reproducibility in biosciences, namely reproducibility of machine learning Artificial Intelligence (AI), reproducibility of translation from research to medical care, and the fundamental challenges to reproducibility. All current chapters are expanded to cover advances in the topics previously addressed. This book provides biomedical researchers with a framework to better understand the reproducibility challenges in the area. Newly introduced interactive exercises and updated case studies help students understand the fundamental concepts involved in the area.
Key Features
- Includes four new chapters and updates across the book, covering recent developments of issues affecting reproducibility in biomedical research
- Covers reproducibility of results from machine learning AI algorithms
- Presents new case studies to illustrate challenges in related fields
- Includes a companion website with interactive exercises and summary tables
2. The Problem of Irreproducibility
3. Validity of Biomedical Science, Reproducibility, and Irreproducibility
4. The Logic of Certainty versus the Logic of Discovery
5. The Logic of Probability and Statistics
6. Causation, Process Metaphor, and Reductionism
7. Case Studies in Clinical Biomedical Research 8. Case Studies in Basic Biomedical Research
9. Case Studies in Computational Biomedical Research
10. Case Studies in Machine Learning Artificial Intelligence (AI)
11. Reproducibility of translation from biomedical research to clinical care
12. Chaotic and Complex Systems, Statistics, and Far-from-Equilibrium Thermodynamics
13. The Nature of the Fundamental Challenges to Reproducibility in any Knowledge Discipline
14. Epilogue
15. Appendix: Types of lack of reproducibility
Graduate students and researchers in biomedical areas, Clinicians, policymakers, grant administrators
