Translational Plastic Surgery
Will be published approx. on 1. January 2026
Book
Paperback/Softback
780 pages
978-0-323-91168-9 (ISBN)
Description
Translational Plastic Surgery provides a comprehensive overview reflecting the depth and breadth of the field of translational research focused on plastic surgery, with input from a distinguished team of basic and clinical investigators. The practical, straightforward approach helps the aspiring investigator navigate challenging considerations in study design and implementation. The book provides valuable discussions of the critical appraisal of published studies in translational plastic surgery research, allowing the reader to learn how to evaluate the quality of such studies with respect to measuring outcomes and to make effective use of all.
More details
Series
Language
English
Place of publication
Oxford
United Kingdom
Publishing group
Elsevier Science & Technology
Target group
Professional and scholarly
Dimensions
Height: 276 mm
Width: 214 mm
Thickness: 32 mm
Weight
1588 gr
ISBN-13
978-0-323-91168-9 (9780323911689)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
MD Eltorai | Jeffrey A. Bakal | Paul Liu
Translational Plastic Surgery
E-Book
01/2026
Elsevier
€143.00
Available for download
Persons
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).
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).
Content
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
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