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Author:   Adam E.M. Eltorai (Harvard Medical School, Boston, MA, USA) ,  Jeffrey A. Bakal, PhD (Division General Internal Medicine, Faculty of Medicine and Dentistry, University of Alberta, Edmonton Alberta, Canada) ,  Paul Liu (Chairman, Division of Plastic and Reconstructive Surgery, Brown University Professor, Surgery of Brown University, USA) ,  Loree Kalliainen
Publisher:   Elsevier Science & Technology
Imprint:   Academic Press Inc
ISBN:  

9780323911689

ISBN 10:   0323911684
Pages:   780
Publication Date:   23 January 2026
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Forthcoming
Availability:   Not yet available  
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Table of Contents

INTRODUCTION 1. Introduction 2. Translational Process 3. Scientific Method 4. Basic research PRE-CLINCIAL 5. Overview of preclinical research 6. What problem are you solving? 7. Types of interventions 8. Drug discovery 9. Drug testing 10. Device discovery and prototyping 11. Device testing 12. Diagnostic discovery 13. Diagnostic testing 14. Other product types 15. Procedural technique development 16. Behavioral intervention CLINICAL: FUNDAMENTALS 17. Introduction to clinical research: What is it? Why is it needed? 18. The question: Types of research questions and how to develop them 19. Study population: Who and why them? 20. Outcome measurements: What data is being collected and why? 21. Optimizing the question: Balancing significance and feasibility STATISTICAL PRINCIPLES 22. Common issues in analysis 23. Basic statistical principles 24. Distributions 25. Hypotheses and error types 26. Power 27. Regression 28. Continuous variable analyses: t-test, Man Whitney, Wilcoxin rank 29. Categorical variable analyses: Chi-square, fisher exact, Mantel hanzel 30. Analysis of variance 31. Correlation 32. Biases 33. Basic science statistics CLINICAL: STUDY TYPES 34. Design principles: Hierarchy of study types 35. Case series: Design, measures, classic example 36. Case-control study: Design, measures, classic example 37. Cohort study: Design, measures, classic example 38. Cross-section study: Design, measures, classic example 39. Longitudinal study: Design, measures, classic example 40. Clinical trials: Design, measures, classic example 41. Meta-analysis: Design, measures, classic example 42. Cost-effectiveness study: Design, measures, classic example 43. Diagnostic test evaluation: Design, measures, classic example 44. Reliability study: Design, measures, classic example 45. Database studies: Design, measures, classic example 46. Surveys and questionnaires: Design, measures, classic example 47. Qualitative methods and mixed methods CLINICAL TRIALS 48. Randomized control: Design, measures, classic example 49. Nonrandomized control: Design, measures, classic example 50. Historical control: Design, measures, classic example 51. Cross-over: Design, measures, classic example 52. Withdrawal studies: Design, measures, classic example 53. Factorial design: Design, measures, classic example 54. Group allocation: Design, measures, classic example 55. Hybrid design: Design, measures, classic example 56. Large, pragmatic: Design, measures, classic example 57. Equivalence and noninferiority: Design, measures, classic example 58. Adaptive: Design, measures, classic example 59. Randomization: Fixed or adaptive procedures 60. Blinding: Who and how? 61. Multicenter considerations 62. Registries 63. Phases of clinical trials 64. IDEAL Framework 65. Artificial Intelligence 66. Patient perspectives CLINICAL: PREPARATION 67. Sample size 68. Budgeting 69. Ethics and review boards 70. Regulatory considerations for new drugs and devices 71. Funding approaches 72. Subject recruitment 73. Data management 74. Quality control 75. Statistical software 76. Report forms: Harm and Quality of Life 77. Subject adherence 78. Survival analysis 79. Monitoring committee in clinical trials REGULATORY BASICS 80. FDA overview 81. IND 82. New drug application 83. Devices 84. Radiation-emitting electronic products 85. Orphan drugs 86. Biologics 87. Combination products 88. Foods 89. Cosmetics 90. CMC and GxP 91. Non-US regulatory 92. Post-Market Drug Safety Monitoring 93. Post-Market Device Safety Monitoring CLINICAL IMPLEMENTATION 94. Implementation Research 95. Design and analysis 96. Mixed-methods research 97. Population- and setting-specific implementation PUBLIC HEALTH 98. Public Health 99. Epidemiology 100. Factors 101. Good questions 102. Population- and environmental-specific considerations 103. Law, policy, and ethics 104. Healthcare institutions and systems 105. Public health institutions and systems 106. Presenting data 107. Manuscript preparation ​​​​​​​108. Building a team 109. Patent basics 110. Venture pathways 111. SBIR/STTR 112. Sample forms and templates

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Author Information

Dr Adam E. M. Eltorai, MD, PhD completed his graduate studies in Biomedical Engineering and Biotechnology along with his medical degree from Brown University. His work has spanned the translational spectrum with a focus on medical technology innovation and development. Dr. Eltorai has published numerous articles and books. Dr Jeff Bakal PhD, P.Stat. is the Program Director for Provincial Research Data Services at Alberta Health Services which operates the Alberta Strategy for Patient Oriented Research (SPOR) data platform and Health Service Statistical & Analytics Methods teams. He has over 10 years of experience working with Health Services data and Randomized Clinical Trials. He completed his PhD jointly with the Department of Mathematics and Statistics and the School of Physical Health and Education at Queen's University. He has worked on the methodology and analysis of several international studies in business strategy, ophthalmology, cardiology, geriatric medicine and the analysis of kinematic data resulting in several peer reviewed articles and conference presentations. His current interests are in developing statistical methodology for time-to-event data and the development of classification tools to assist in patient decision making processes. Paul Liu, MD, is Chairman of the Division of Plastic and Reconstructive Surgery at Brown University and Professor of Surgery of Brown University. He earned his medical degree from Harvard Medical School and completed his residencies in general and plastic surgery at Brigham and Women’s Hospital. Dr. Liu has extensive basic science research interests including the use of genetic manipulation of the wound environment to speed healing and using mathematical modeling to accelerate the development of new wound therapeutics. Dr. Liu has developed a research collaboration with mathematicians from Oxford, Nottingham, the University of Southern California, as well as scientists in China to accomplish the latter goal. He was recently awarded Top Doctor from Rhode Island Monthly (2019).

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