Adam E.M. Eltorai,
Jeffrey A. Bakal,
Paul Liu,
Loree Kalliainen,
Jung Ho Gong
e.a.
Elsevier Science
e druk, 2026
9780323911689
Translational Plastic Surgery
Specificaties
Paperback, blz.
|
Engels
Elsevier Science |
e druk, 2026
ISBN13: 9780323911689
Rubricering
Onderdeel van serie
Handbook for Designing and Conducting Clinical and Translational Research
Levertijd ongeveer 8 werkdagen
Samenvatting
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.
Specificaties
Inhoudsopgave
INTRODUCTION<br>1. Introduction<br>2. Translational Process<br>3. Scientific Method<br>4. Basic research<br><br>PRE-CLINCIAL<br>5. Overview of preclinical research<br>6. What problem are you solving?<br>7. Types of interventions<br>8. Drug discovery<br>9. Drug testing<br>10. Device discovery and prototyping<br>11. Device testing<br>12. Diagnostic discovery<br>13. Diagnostic testing<br>14. Other product types<br>15. Procedural technique development<br>16. Behavioral intervention<br><br>CLINICAL: FUNDAMENTALS<br>17. Introduction to clinical research: What is it? Why is it needed?<br>18. The question: Types of research questions and how to develop them<br>19. Study population: Who and why them?<br>20. Outcome measurements: What data is being collected and why?<br>21. Optimizing the question: Balancing significance and feasibility<br><br>STATISTICAL PRINCIPLES<br>22. Common issues in analysis<br>23. Basic statistical principles<br>24. Distributions<br>25. Hypotheses and error types<br>26. Power<br>27. Regression<br>28. Continuous variable analyses: t-test, Man Whitney, Wilcoxin rank<br>29. Categorical variable analyses: Chi-square, fisher exact, Mantel hanzel<br>30. Analysis of variance<br>31. Correlation<br>32. Biases<br>33. Basic science statistics<br><br>CLINICAL: STUDY TYPES<br>34. Design principles: Hierarchy of study types<br>35. Case series: Design, measures, classic example<br>36. Case-control study: Design, measures, classic example<br>37. Cohort study: Design, measures, classic example<br>38. Cross-section study: Design, measures, classic example<br>39. Longitudinal study: Design, measures, classic example<br>40. Clinical trials: Design, measures, classic example<br>41. Meta-analysis: Design, measures, classic example<br>42. Cost-effectiveness study: Design, measures, classic example<br>43. Diagnostic test evaluation: Design, measures, classic example<br>44. Reliability study: Design, measures, classic example<br>45. Database studies: Design, measures, classic example<br>46. Surveys and questionnaires: Design, measures, classic example<br>47. Qualitative methods and mixed methods<br><br>CLINICAL TRIALS<br>48. Randomized control: Design, measures, classic example<br>49. Nonrandomized control: Design, measures, classic example<br>50. Historical control: Design, measures, classic example<br>51. Cross-over: Design, measures, classic example<br>52. Withdrawal studies: Design, measures, classic example<br>53. Factorial design: Design, measures, classic example<br>54. Group allocation: Design, measures, classic example<br>55. Hybrid design: Design, measures, classic example<br>56. Large, pragmatic: Design, measures, classic example<br>57. Equivalence and noninferiority: Design, measures, classic example<br>58. Adaptive: Design, measures, classic example<br>59. Randomization: Fixed or adaptive procedures<br>60. Blinding: Who and how?<br>61. Multicenter considerations<br>62. Registries<br>63. Phases of clinical trials<br>64. IDEAL Framework<br>65. Artificial Intelligence<br>66. Patient perspectives<br><br>CLINICAL: PREPARATION<br>67. Sample size<br>68. Budgeting<br>69. Ethics and review boards<br>70. Regulatory considerations for new drugs and devices<br>71. Funding approaches<br>72. Subject recruitment<br>73. Data management<br>74. Quality control<br>75. Statistical software<br>76. Report forms: Harm and Quality of Life<br>77. Subject adherence<br>78. Survival analysis<br>79. Monitoring committee in clinical trials<br><br>REGULATORY BASICS<br>80. FDA overview<br>81. IND<br>82. New drug application<br>83. Devices<br>84. Radiation-emitting electronic products<br>85. Orphan drugs<br>86. Biologics<br>87. Combination products<br>88. Foods<br>89. Cosmetics<br>90. CMC and GxP<br>91. Non-US regulatory<br>92. Post-Market Drug Safety Monitoring<br>93. Post-Market Device Safety Monitoring<br><br>CLINICAL IMPLEMENTATION<br>94. Implementation Research<br>95. Design and analysis<br>96. Mixed-methods research<br>97. Population- and setting-specific implementation<br><br>PUBLIC HEALTH<br>98. Public Health<br>99. Epidemiology<br>100. Factors<br>101. Good questions<br>102. Population- and environmental-specific considerations<br>103. Law, policy, and ethics<br>104. Healthcare institutions and systems<br>105. Public health institutions and systems<br>106. Presenting data<br>107. Manuscript preparation<br>108. Building a team<br>109. Patent basics<br>110. Venture pathways<br>111. SBIR/STTR<br>112. Sample forms and templates