Navigating Health Research: From Fundamentals to Advanced Practices in Biostatistics is an essential resource for students, scientists, and clinicians embarking on their research journeys. This book clarifies research methodology and regulation while providing foundational knowledge in biostatistics through clear explanations and illustrative examples. Covering both classical and modern advancements in health research, it addresses topics such as health economics, artificial intelligence, and bioinformatics. The book offers a step-by-step guide through the research lifecycle, tackling challenges like study design flaws and ethical compliance. Emphasizing open-source software and freely available resources, it ensures accessibility for researchers from diverse socioeconomic backgrounds. With a multi-generational and multicultural perspective, this book enhances its relevance for a broad audience. By simplifying complex topics and promoting best practices, it empowers early-career researchers and clinicians to improve their skills, elevate the quality of their work, and contribute meaningfully to evidence-based practice in health research.
1. Introduction - p. xxiii 2. Chapter 1. Basic aspects in biomedical research methodology - p. 1 3. Chapter 2. The art of “re” and “search” in research - p. 11 4. Chapter 3. Selecting the optimal study design! - p. 59 5. Intended learning outcomes (ILOs) - p. 59 6. Author's contribution - p. 60 7. 3.1 Introduction to healthcare research and study design - p. 60 8. 3.2 Observational studies - p. 64 9. 3.3 Prognostic studies - p. 80 10. 3.4 Diagnostic studies - p. 98 11. 3.5 Interventional studies - p. 110 12. 3.6 Pilot and feasibility studies - p. 150 13. 3.7 Causal study designs - p. 153 14. 3.8 Review studies - p. 161 15. 3.9 Pharmacoeconomics - p. 182 16. 3.10 Animal experiments - p. 200 17. 3.11 In vitro testing - p. 221 18. 3.12 Measuring variables of interest - p. 238 19. 3.13 New trends in clinical research - p. 269 20. Chapter 4. Write the protocol - p. 329 21. Intended learning objectives (ILOs) - p. 329 22. 4.1 Background and rationale - p. 333 23. 4.2 Objectives - p. 335 24. 4.3 Study design - p. 335 25. 4.4 Methods - p. 368 26. 4.5 Study setting, population, and sampling - p. 375 27. 4.6 Treatments and interventions - p. 382 28. Author's contribution - p. 384 29. 4.7 Data management - p. 384 30. 4.8 Statistical methods - p. 399 31. 4.9 Research tools - p. 409 32. 4.10 Study documentation - p. 415 33. 4.11 Study ethics - p. 420 34. 4.12 Data sharing, registration, and publication - p. 423 35. 4.13 Amendments - p. 426 36. 4.14 Grant proposal - p. 428 37. AI disclosure - p. 432 38. Acknowledgement - p. 432 39. Chapter 5. Consider the ethics - p. 443 40. Chapter 6. Plan the statistical analysis - p. 481 41. Intended learning outcomes (ILOs) - p. 481 42. Author's contribution - p. 481 43. 6.1 Descriptive statistics - p. 481 44. 6.2 Data management - p. 496 45. 6.3 Inferential statistics - p. 501 46. 6.3.1 The null and the alternative hypotheses - p. 501 47. 6.3.2 The primary risk of error - p. 502 48. 6.3.3 The secondary risk of error - p. 503 49. 6.3.4 The direction of the study - p. 503 50. 6.3.5 The statistical software - p. 504 51. 6.3.6 Choosing the appropriate statistical test - p. 505 52. 6.3.7 The bivariate statistical tests - p. 505 53. 6.3.7.1 Testing the distribution of qualitative variables in independent groups (unpaired tests) - p. 507 54. 6.3.7.2 Testing the distribution of qualitative variables in dependent groups (paired groups) - p. 517 55. 6.3.7.3 Testing the distribution of quantitative variables among independent groups (unpaired tests) - p. 522 56. 6.3.7.4 Paired tests for quantitative variables - p. 543 57. 6.3.7.5 Relationship between two quantitative variables - p. 553 58. 6.3.7.6 Survival analysis - p. 562 59. 6.3.7.7 Measuring agreement - p. 571 60. 6.3.7.8 Diagnostic accuracy measures - p. 593 61. 6.3.8 The multivariable statistical tests - p. 605 62. 6.4 Propensity score analysis - p. 654 63. 6.5 Meta-analysis - p. 669 64. AI disclosure - p. 697 65. Chapter 7. Calculate the sample size - p. 703 66. Intended learning outcomes (ILOs) - p. 703 67. 7.1 Basis and rationale - p. 703 68. 7.2 Choosing the primary outcome - p. 705 69. 7.3 Defining the effect size - p. 711 70. 7.4 Sample size calculation: A generic formula - p. 717 71. 7.5 Sample size calculation for different outcome types - p. 734 72. 7.6 The ten commandments - p. 779 73. Chapter 8. Disseminate knowledge - p. 791 74. Chapter 9. From evidence to practice: Knowledge translation, barriers, and research limitations - p. 829 75. Chapter 10. Pitfalls, common errors, and false interpretation - p. 849 76. List of abbreviations - p. 881 77. Glossary - p. 889 78. Index - p. 909
Navigating Health Research: From Fundamentals to Advanced Practices in Biostatistics is an essential resource for students, scientists, and clinicians embarking on their research journeys. This book clarifies research methodology and regulation while providing foundational knowledge in biostatistics through clear explanations and illustrative examples. Covering both classical and modern advancements in health research, it addresses topics such as health economics, artificial intelligence, and bioinformatics. The book offers a step-by-step guide through the research lifecycle, tackling challenges like study design flaws and ethical compliance. Emphasizing open-source software and freely available resources, it ensures accessibility for researchers from diverse socioeconomic backgrounds. With a multi-generational and multicultural perspective, this book enhances its relevance for a broad audience. By simplifying complex topics and promoting best practices, it empowers early-career researchers and clinicians to improve their skills, elevate the quality of their work, and contribute meaningfully to evidence-based practice in health research.
1. Introduction - p. xxiii 2. Chapter 1. Basic aspects in biomedical research methodology - p. 1 3. Chapter 2. The art of “re” and “search” in research - p. 11 4. Chapter 3. Selecting the optimal study design! - p. 59 5. Intended learning outcomes (ILOs) - p. 59 6. Author's contribution - p. 60 7. 3.1 Introduction to healthcare research and study design - p. 60 8. 3.2 Observational studies - p. 64 9. 3.3 Prognostic studies - p. 80 10. 3.4 Diagnostic studies - p. 98 11. 3.5 Interventional studies - p. 110 12. 3.6 Pilot and feasibility studies - p. 150 13. 3.7 Causal study designs - p. 153 14. 3.8 Review studies - p. 161 15. 3.9 Pharmacoeconomics - p. 182 16. 3.10 Animal experiments - p. 200 17. 3.11 In vitro testing - p. 221 18. 3.12 Measuring variables of interest - p. 238 19. 3.13 New trends in clinical research - p. 269 20. Chapter 4. Write the protocol - p. 329 21. Intended learning objectives (ILOs) - p. 329 22. 4.1 Background and rationale - p. 333 23. 4.2 Objectives - p. 335 24. 4.3 Study design - p. 335 25. 4.4 Methods - p. 368 26. 4.5 Study setting, population, and sampling - p. 375 27. 4.6 Treatments and interventions - p. 382 28. Author's contribution - p. 384 29. 4.7 Data management - p. 384 30. 4.8 Statistical methods - p. 399 31. 4.9 Research tools - p. 409 32. 4.10 Study documentation - p. 415 33. 4.11 Study ethics - p. 420 34. 4.12 Data sharing, registration, and publication - p. 423 35. 4.13 Amendments - p. 426 36. 4.14 Grant proposal - p. 428 37. AI disclosure - p. 432 38. Acknowledgement - p. 432 39. Chapter 5. Consider the ethics - p. 443 40. Chapter 6. Plan the statistical analysis - p. 481 41. Intended learning outcomes (ILOs) - p. 481 42. Author's contribution - p. 481 43. 6.1 Descriptive statistics - p. 481 44. 6.2 Data management - p. 496 45. 6.3 Inferential statistics - p. 501 46. 6.3.1 The null and the alternative hypotheses - p. 501 47. 6.3.2 The primary risk of error - p. 502 48. 6.3.3 The secondary risk of error - p. 503 49. 6.3.4 The direction of the study - p. 503 50. 6.3.5 The statistical software - p. 504 51. 6.3.6 Choosing the appropriate statistical test - p. 505 52. 6.3.7 The bivariate statistical tests - p. 505 53. 6.3.7.1 Testing the distribution of qualitative variables in independent groups (unpaired tests) - p. 507 54. 6.3.7.2 Testing the distribution of qualitative variables in dependent groups (paired groups) - p. 517 55. 6.3.7.3 Testing the distribution of quantitative variables among independent groups (unpaired tests) - p. 522 56. 6.3.7.4 Paired tests for quantitative variables - p. 543 57. 6.3.7.5 Relationship between two quantitative variables - p. 553 58. 6.3.7.6 Survival analysis - p. 562 59. 6.3.7.7 Measuring agreement - p. 571 60. 6.3.7.8 Diagnostic accuracy measures - p. 593 61. 6.3.8 The multivariable statistical tests - p. 605 62. 6.4 Propensity score analysis - p. 654 63. 6.5 Meta-analysis - p. 669 64. AI disclosure - p. 697 65. Chapter 7. Calculate the sample size - p. 703 66. Intended learning outcomes (ILOs) - p. 703 67. 7.1 Basis and rationale - p. 703 68. 7.2 Choosing the primary outcome - p. 705 69. 7.3 Defining the effect size - p. 711 70. 7.4 Sample size calculation: A generic formula - p. 717 71. 7.5 Sample size calculation for different outcome types - p. 734 72. 7.6 The ten commandments - p. 779 73. Chapter 8. Disseminate knowledge - p. 791 74. Chapter 9. From evidence to practice: Knowledge translation, barriers, and research limitations - p. 829 75. Chapter 10. Pitfalls, common errors, and false interpretation - p. 849 76. List of abbreviations - p. 881 77. Glossary - p. 889 78. Index - p. 909
