Translational Plastic Surgery

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. Bakal 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. He also leads the Health Service Statistical and Analytics Methods teams. Over 10 years, he has worked with health services data, including randomized clinical trials. He completed his PhD jointly through the Department of Mathematics and Statistics and the School of Physical Health and Education at Queen’s University. His work includes methodological contributions and analyses supporting international studies spanning business strategy, ophthalmology, cardiology, and geriatric medicine, as well as analyses of kinematic data, through peer-reviewed publications and conference presentations. Current interests include developing statistical methodology for time-to-event data and building classification tools to support 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).

• INTRODUCTION1. Introduction2. Translational Process3. Scientific Method4. Basic researchPRE-CLINCIAL5. Overview of preclinical research6. What problem are you solving?7. Types of interventions8. Drug discovery9. Drug testing10. Device discovery and prototyping11. Device testing12. Diagnostic discovery13. Diagnostic testing14. Other product types15. Procedural technique development16. Behavioral interventionCLINICAL: FUNDAMENTALS17. Introduction to clinical research: What is it? Why is it needed?18. The question: Types of research questions and how to develop them19. Study population: Who and why them?20. Outcome measurements: What data is being collected and why?21. Optimizing the question: Balancing significance and feasibilitySTATISTICAL PRINCIPLES22. Common issues in analysis23. Basic statistical principles24. Distributions25. Hypotheses and error types26. Power27. Regression28. Continuous variable analyses: t-test, Man Whitney, Wilcoxin rank29. Categorical variable analyses: Chi-square, fisher exact, Mantel hanzel30. Analysis of variance31. Correlation32. Biases33. Basic science statisticsCLINICAL: STUDY TYPES34. Design principles: Hierarchy of study types35. Case series: Design, measures, classic example36. Case-control study: Design, measures, classic example37. Cohort study: Design, measures, classic example38. Cross-section study: Design, measures, classic example39. Longitudinal study: Design, measures, classic example40. Clinical trials: Design, measures, classic example41. Meta-analysis: Design, measures, classic example42. Cost-effectiveness study: Design, measures, classic example43. Diagnostic test evaluation: Design, measures, classic example44. Reliability study: Design, measures, classic example45. Database studies: Design, measures, classic example46. Surveys and questionnaires: Design, measures, classic example47. Qualitative methods and mixed methodsCLINICAL TRIALS48. Randomized control: Design, measures, classic example49. Nonrandomized control: Design, measures, classic example50. Historical control: Design, measures, classic example51. Cross-over: Design, measures, classic example52. Withdrawal studies: Design, measures, classic example53. Factorial design: Design, measures, classic example54. Group allocation: Design, measures, classic example55. Hybrid design: Design, measures, classic example56. Large, pragmatic: Design, measures, classic example57. Equivalence and noninferiority: Design, measures, classic example58. Adaptive: Design, measures, classic example59. Randomization: Fixed or adaptive procedures60. Blinding: Who and how?61. Multicenter considerations62. Registries63. Phases of clinical trials64. IDEAL Framework65. Artificial Intelligence66. Patient perspectivesCLINICAL: PREPARATION67. Sample size68. Budgeting69. Ethics and review boards70. Regulatory considerations for new drugs and devices71. Funding approaches72. Subject recruitment73. Data management74. Quality control75. Statistical software76. Report forms: Harm and Quality of Life77. Subject adherence78. Survival analysis79. Monitoring committee in clinical trialsREGULATORY BASICS80. FDA overview81. IND82. New drug application83. Devices84. Radiation-emitting electronic products85. Orphan drugs86. Biologics87. Combination products88. Foods89. Cosmetics90. CMC and GxP91. Non-US regulatory92. Post-Market Drug Safety Monitoring93. Post-Market Device Safety MonitoringCLINICAL IMPLEMENTATION94. Implementation Research95. Design and analysis96. Mixed-methods research97. Population- and setting-specific implementationPUBLIC HEALTH98. Public Health99. Epidemiology100. Factors101. Good questions102. Population- and environmental-specific considerations103. Law, policy, and ethics104. Healthcare institutions and systems105. Public health institutions and systems106. Presenting data107. Manuscript preparation108. Building a team109. Patent basics110. Venture pathways111. SBIR/STTR112. Sample forms and templates