Davis Hartnett, MD completed his medical degree at the Warren Alpert Medical School of Brown University and is a resident physician at Brigham and Women’s Hospital in Boston, Massachusetts in the Department of Internal Medicine. He has contributed to over 40 peer reviewed publications and over 30 academic presentations on research topics including medicolegal outcomes, healthcare access disparities, and translational medicine.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. 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.Larisa Tereshchenko is an Associate Professor of Medicine at the Cleveland Clinic Lerner College of Medicine of Case Western Reserve University and an Adjunct Associate Professor of Electrical Engineering and Computer Science at the Cleveland State University. She has a broad background in clinical investigation, cardiology, cardiac electrophysiology and electrocardiology, biomedical engineering, biophysics, randomized controlled trials, epidemiology, biostatistics, bioinformatics, and genomics. Over the past two decades, she has led clinical studies, including randomized controlled trials, cohort, and case-control studies, and has expertise in multicenter and multidisciplinary research leadership, the building of collaborative groups, and multicohort epidemiological studies. She is an author of more than 160 original peer-reviewed manuscripts, chapters, and reviews.

• Introduction 1. Introduction2. Translational process3. Scientific MethodPre-Clinical 4. Overview of Preclinical Research5. What Problem are You Solving?6. Types of Interventions7. Drug Discovery8. Drug Testing9. Device Discovery and Prototyping10. Device Testing11. Diagnostic Discovery12. Other Product Types13. Procedural Technique Development14. Behavioral Intervention15. Artificial IntelligenceClinical: Fundamentals 16. Introduction to Clinical Research: What is it? Why is it Needed?17. The Question: Types of Research Questions and how to Development Them18. Study Population: Who and Why Them?19. Outcome Measurements: What Data is Being Collected and Why?20. Optimizing the Question: Balancing Significance and FeasibilityStatistical Principles 21. Common Issues in Analysis22. Basic Statistical Principles23. Hypotheses and Error Types24. Power25. Regression26. Continuous Variable Analyses27. Categorical Variable Analyses: Chi-square, Fisher Exact, Mantel Hanzel28. Correlation29. Biases30. Basic Science Statistics31. Sample Size32. Statistical SoftwareClinical: Study Types 33. Design Principles: Hierarchy of Study Types34. Case Series: Design, Measures, Classic Example35. Case-Control Study: Design, Measures, Classic Example36. Cohort Study: Design, Measures, Classic Example37. Cross-section Study: Design, Measures, Classic Example38. Longitudinal Study: Design Measures, Classic Example39. Meta-analysis: Design, Measures, Classic Example40. Cost-effectiveness Study: Design, Measures, Classic Example41. Diagnostic Test Evaluation: Design, Measures, Classic Example42. Reliability Study: Design, Measures, Classic Example43. Database Studies: Design, Measures, Classic Example44. Surveys and Questionnaires: Design, Measures, Classic Example45. Qualitative Methods and Mixed MethodsClinical Trials 46. Randomized Control: Design, Measures, Classic Example47. Historical Control: Design, Measures, Classic Example48. Cross-Over: Design, Measures, Classic Example49. Withdrawal Studies: Design, Measures, Classic Example50. Factorial Design: Design, Measures, Classic Example51. Group Allocation: Design, Measures, Classic Example52. Hybrid Design: Design, Measures, Classic Example53. Large, Pragmatic: Design, Measures, Classic Example54. Equivalence and Noninferiority: Design, Measures, Classic Example55. Adaptive: Design, Measures, Classic Example56. Phase 0 Trials: Window of Opportunity57. Registries58. Phases of Clinical Trials59. IDEAL FrameworkClinical: Preparation 60. Patient Perspectives61. Budgeting62. Ethics and Review Boards63. Regulatory Considerations for New Drugs and Devices64. Funding Approaches65. Conflicts of Interest66. Subject Recruitment67. Data Management68. Special Populations69. Subject Adherence70. Survival Analysis71. Monitoring Committee in Clinical TrialsRegulatory Basics 72. FDA Overview73. IND74. New Drug Application75. Devices76. Orphan Drugs77. Biologics78. Combination Products79. Foods80. Cosmetics81. CMC and GxP82. Non-US Regulatory83. Post-Market Drug Safety Monitoring84. Post-Market Device Safety MonitoringClinical Implementation 85. Implementation Research86. Design and Analysis87. Mixed-methods Research88. Population- and Setting-specific Implementation89. Guideline DevelopmentPublic Health 90. Public Health91. Epidemiology92. Factors93. Good Questions94. Population- and Environmental-specific Considerations95. Law, Policy, and Ethics96. Healthcare Institutions and Systems97. Public Health Institutions and SystemsPractical Resources 98. Presenting Data99. Manuscript Preparation100. Quality Improvement101. Team Science and Building a Team102. Patent Basics103. Venture Pathways104. SBIR/STTR105. Sample Forms and Templates

Davis Hartnett, MD completed his medical degree at the Warren Alpert Medical School of Brown University and is a resident physician at Brigham and Women’s Hospital in Boston, Massachusetts in the Department of Internal Medicine. He has contributed to over 40 peer reviewed publications and over 30 academic presentations on research topics including medicolegal outcomes, healthcare access disparities, and translational medicine.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. 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.Larisa Tereshchenko is an Associate Professor of Medicine at the Cleveland Clinic Lerner College of Medicine of Case Western Reserve University and an Adjunct Associate Professor of Electrical Engineering and Computer Science at the Cleveland State University. She has a broad background in clinical investigation, cardiology, cardiac electrophysiology and electrocardiology, biomedical engineering, biophysics, randomized controlled trials, epidemiology, biostatistics, bioinformatics, and genomics. Over the past two decades, she has led clinical studies, including randomized controlled trials, cohort, and case-control studies, and has expertise in multicenter and multidisciplinary research leadership, the building of collaborative groups, and multicohort epidemiological studies. She is an author of more than 160 original peer-reviewed manuscripts, chapters, and reviews.

• Introduction 1. Introduction2. Translational process3. Scientific MethodPre-Clinical 4. Overview of Preclinical Research5. What Problem are You Solving?6. Types of Interventions7. Drug Discovery8. Drug Testing9. Device Discovery and Prototyping10. Device Testing11. Diagnostic Discovery12. Other Product Types13. Procedural Technique Development14. Behavioral Intervention15. Artificial IntelligenceClinical: Fundamentals 16. Introduction to Clinical Research: What is it? Why is it Needed?17. The Question: Types of Research Questions and how to Development Them18. Study Population: Who and Why Them?19. Outcome Measurements: What Data is Being Collected and Why?20. Optimizing the Question: Balancing Significance and FeasibilityStatistical Principles 21. Common Issues in Analysis22. Basic Statistical Principles23. Hypotheses and Error Types24. Power25. Regression26. Continuous Variable Analyses27. Categorical Variable Analyses: Chi-square, Fisher Exact, Mantel Hanzel28. Correlation29. Biases30. Basic Science Statistics31. Sample Size32. Statistical SoftwareClinical: Study Types 33. Design Principles: Hierarchy of Study Types34. Case Series: Design, Measures, Classic Example35. Case-Control Study: Design, Measures, Classic Example36. Cohort Study: Design, Measures, Classic Example37. Cross-section Study: Design, Measures, Classic Example38. Longitudinal Study: Design Measures, Classic Example39. Meta-analysis: Design, Measures, Classic Example40. Cost-effectiveness Study: Design, Measures, Classic Example41. Diagnostic Test Evaluation: Design, Measures, Classic Example42. Reliability Study: Design, Measures, Classic Example43. Database Studies: Design, Measures, Classic Example44. Surveys and Questionnaires: Design, Measures, Classic Example45. Qualitative Methods and Mixed MethodsClinical Trials 46. Randomized Control: Design, Measures, Classic Example47. Historical Control: Design, Measures, Classic Example48. Cross-Over: Design, Measures, Classic Example49. Withdrawal Studies: Design, Measures, Classic Example50. Factorial Design: Design, Measures, Classic Example51. Group Allocation: Design, Measures, Classic Example52. Hybrid Design: Design, Measures, Classic Example53. Large, Pragmatic: Design, Measures, Classic Example54. Equivalence and Noninferiority: Design, Measures, Classic Example55. Adaptive: Design, Measures, Classic Example56. Phase 0 Trials: Window of Opportunity57. Registries58. Phases of Clinical Trials59. IDEAL FrameworkClinical: Preparation 60. Patient Perspectives61. Budgeting62. Ethics and Review Boards63. Regulatory Considerations for New Drugs and Devices64. Funding Approaches65. Conflicts of Interest66. Subject Recruitment67. Data Management68. Special Populations69. Subject Adherence70. Survival Analysis71. Monitoring Committee in Clinical TrialsRegulatory Basics 72. FDA Overview73. IND74. New Drug Application75. Devices76. Orphan Drugs77. Biologics78. Combination Products79. Foods80. Cosmetics81. CMC and GxP82. Non-US Regulatory83. Post-Market Drug Safety Monitoring84. Post-Market Device Safety MonitoringClinical Implementation 85. Implementation Research86. Design and Analysis87. Mixed-methods Research88. Population- and Setting-specific Implementation89. Guideline DevelopmentPublic Health 90. Public Health91. Epidemiology92. Factors93. Good Questions94. Population- and Environmental-specific Considerations95. Law, Policy, and Ethics96. Healthcare Institutions and Systems97. Public Health Institutions and SystemsPractical Resources 98. Presenting Data99. Manuscript Preparation100. Quality Improvement101. Team Science and Building a Team102. Patent Basics103. Venture Pathways104. SBIR/STTR105. Sample Forms and Templates

Davis Hartnett, MD completed his medical degree at the Warren Alpert Medical School of Brown University and is a resident physician at Brigham and Women’s Hospital in Boston, Massachusetts in the Department of Internal Medicine. He has contributed to over 40 peer reviewed publications and over 30 academic presentations on research topics including medicolegal outcomes, healthcare access disparities, and translational medicine.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. 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.Larisa Tereshchenko is an Associate Professor of Medicine at the Cleveland Clinic Lerner College of Medicine of Case Western Reserve University and an Adjunct Associate Professor of Electrical Engineering and Computer Science at the Cleveland State University. She has a broad background in clinical investigation, cardiology, cardiac electrophysiology and electrocardiology, biomedical engineering, biophysics, randomized controlled trials, epidemiology, biostatistics, bioinformatics, and genomics. Over the past two decades, she has led clinical studies, including randomized controlled trials, cohort, and case-control studies, and has expertise in multicenter and multidisciplinary research leadership, the building of collaborative groups, and multicohort epidemiological studies. She is an author of more than 160 original peer-reviewed manuscripts, chapters, and reviews.

• Introduction 1. Introduction2. Translational process3. Scientific MethodPre-Clinical 4. Overview of Preclinical Research5. What Problem are You Solving?6. Types of Interventions7. Drug Discovery8. Drug Testing9. Device Discovery and Prototyping10. Device Testing11. Diagnostic Discovery12. Other Product Types13. Procedural Technique Development14. Behavioral Intervention15. Artificial IntelligenceClinical: Fundamentals 16. Introduction to Clinical Research: What is it? Why is it Needed?17. The Question: Types of Research Questions and how to Development Them18. Study Population: Who and Why Them?19. Outcome Measurements: What Data is Being Collected and Why?20. Optimizing the Question: Balancing Significance and FeasibilityStatistical Principles 21. Common Issues in Analysis22. Basic Statistical Principles23. Hypotheses and Error Types24. Power25. Regression26. Continuous Variable Analyses27. Categorical Variable Analyses: Chi-square, Fisher Exact, Mantel Hanzel28. Correlation29. Biases30. Basic Science Statistics31. Sample Size32. Statistical SoftwareClinical: Study Types 33. Design Principles: Hierarchy of Study Types34. Case Series: Design, Measures, Classic Example35. Case-Control Study: Design, Measures, Classic Example36. Cohort Study: Design, Measures, Classic Example37. Cross-section Study: Design, Measures, Classic Example38. Longitudinal Study: Design Measures, Classic Example39. Meta-analysis: Design, Measures, Classic Example40. Cost-effectiveness Study: Design, Measures, Classic Example41. Diagnostic Test Evaluation: Design, Measures, Classic Example42. Reliability Study: Design, Measures, Classic Example43. Database Studies: Design, Measures, Classic Example44. Surveys and Questionnaires: Design, Measures, Classic Example45. Qualitative Methods and Mixed MethodsClinical Trials 46. Randomized Control: Design, Measures, Classic Example47. Historical Control: Design, Measures, Classic Example48. Cross-Over: Design, Measures, Classic Example49. Withdrawal Studies: Design, Measures, Classic Example50. Factorial Design: Design, Measures, Classic Example51. Group Allocation: Design, Measures, Classic Example52. Hybrid Design: Design, Measures, Classic Example53. Large, Pragmatic: Design, Measures, Classic Example54. Equivalence and Noninferiority: Design, Measures, Classic Example55. Adaptive: Design, Measures, Classic Example56. Phase 0 Trials: Window of Opportunity57. Registries58. Phases of Clinical Trials59. IDEAL FrameworkClinical: Preparation 60. Patient Perspectives61. Budgeting62. Ethics and Review Boards63. Regulatory Considerations for New Drugs and Devices64. Funding Approaches65. Conflicts of Interest66. Subject Recruitment67. Data Management68. Special Populations69. Subject Adherence70. Survival Analysis71. Monitoring Committee in Clinical TrialsRegulatory Basics 72. FDA Overview73. IND74. New Drug Application75. Devices76. Orphan Drugs77. Biologics78. Combination Products79. Foods80. Cosmetics81. CMC and GxP82. Non-US Regulatory83. Post-Market Drug Safety Monitoring84. Post-Market Device Safety MonitoringClinical Implementation 85. Implementation Research86. Design and Analysis87. Mixed-methods Research88. Population- and Setting-specific Implementation89. Guideline DevelopmentPublic Health 90. Public Health91. Epidemiology92. Factors93. Good Questions94. Population- and Environmental-specific Considerations95. Law, Policy, and Ethics96. Healthcare Institutions and Systems97. Public Health Institutions and SystemsPractical Resources 98. Presenting Data99. Manuscript Preparation100. Quality Improvement101. Team Science and Building a Team102. Patent Basics103. Venture Pathways104. SBIR/STTR105. Sample Forms and Templates