Reproducibility in Biomedical Research

Epistemological and Statistical Problems and the Future

Erwin B. Montgomery Jr.

• Éditeur: Academic Press
• Année: 2024
• ISBN:
  9780443138300
• Édition: 2
• Sujet: Biological Sciences
• Sous-sujet: Biophysics

Reproducibility in Biomedical Research: Epistemological and Statistical Problems, 2nd Ed. explores the ideas and conundrums inherent in scientific research.

Reproducibility is one of the biggest challenges in biomedical research. It affects not only the ability to replicate results, but the very trust in the findings. Since published in 2019, Reproducibility of Biomedical Research: Epistemological and Statistical Problems established itself as a solid ethical reference in the area, leading to significant reflection on biomedical research. The 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 will be expanded to cover advances in the topics previously addressed.

Reproducibility in Biomedical Research: Epistemological and Statistical Problems, 2nd Ed. 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.

• 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

Table des matières

• Cover image
• Title page
• Table of Contents
• Copyright
• Dedication
• Quotes
• Preface to the second edition
• Preface to the first edition
• Chapter 1. Introduction
• Chapter 2. The problem of irreproducibility
• Chapter 3. Validity of biomedical science, reproducibility, and irreproducibility
• Chapter 4. The logic of certainty versus the logic of discovery
• Chapter 5. The logic of probability and statistics
• Chapter 6. Causation, process metaphor, and reductionism
• Chapter 7. Case studies in clinical biomedical research
• Chapter 8. Case studies in basic biomedical research
• Chapter 9. Case studies in computational biomedical research
• Chapter 10. Case studies in translational research
• Chapter 11. Case studies in machine learning artificial intelligence
• Chapter 12. Chaotic and Complex systems, statistics, and far-from-equilibrium thermodynamics
• Chapter 13. The fundamental problem
• Chapter 14. Epilogue
• Appendix A. An introduction to the logic of logic
• Appendix B. Introduction to the logic of probability and statistics
• Appendix C. Moving away from sample-based analyses for translational research
• Glossary
• Index