Clinical Epidemiology — Validity, Errors and Biases

Public Health and Epidemiology · Clinical Bases

How it begins

Un tratado exhaustivo y de nivel experto sobre las bases lógicas, matemáticas y metodológicas que sustentan la inferencia epidemiológica. Desde la disección molecular del error aleatorio y sistemático hasta las estrategias analíticas más avanzadas para neutralizar la confusión y los sesgos en la era del Big Data y la Medicina Basada en la Evidencia.

What it covers

1. Epidemiological Inference and Architecture of Validity
2. · Validity as a Requirement of Inference
3. Error Taxonomy: Random and Systematic
4. · Random Error (Random or Lack of Precision)
5. · Systematic Error (Bias or Lack of Validity)
6. Selection Biases: Taxonomy and Dynamics
7. · The Dynamics of Selection: Recruitment Conditions
8. · Classic Types of Selection Bias
9. · Selection Bias Mitigation Strategies
10. Information Biases and Misclassification
11. · Classic Forms of Information Bias
12. · Methodological Control of Information Biases
13. Confusion Bias: Pathophysiology and DAGs
14. · The Three Classic Criteria of a Confounding Factor
15. · Graphic Representation: Directed Acyclic Graphs (DAGs)
16. · Simpson's Paradox
17. Control of Confusion Bias in the Design Phase
18. · 1. Randomization
19. · 2. Restriction
20. · 3. Pairing or Coupling (Matching)
21. Control of Confusion Bias in the Analysis Phase
22. · 1. Stratification
23. · 2. Multivariable Regression Models
24. · 3. Methods based on the Propensity Score
25. Effect and Interaction Modification
26. · Interaction Evaluation: Additive versus Multiplicative Scale
27. · Calculation of Interaction on Additive Scale: RERI, AP and S
28. Bias in Cohort Studies and Clinical Trials
29. · Bias of Clinical Trials
30. · Specific Biases of Screening Cohorts
31. Bias in Case and Control Studies
32. · 1. Control Selection Biases
33. · 2. Hindsight Information Bias
34. · 3. Cross Selection Control Bias
35. Bias in Systematic Reviews and Meta-Analyses
36. · 1. Publication Bias (The File Drawer Problem)
37. · 2. Heterogeneity in Meta-analysis
38. Bias in the Era of Big Data, RWE and Artificial Intelligence
39. · 1. Immortal Time Bias
40. · 2. Protopathic Bias
41. · 3. Channeling or Indication Bias (Channeling Bias)
42. · 4. Algorithmic Biases in Artificial Intelligence and Machine Learning
43. Quantitative Bias and Sensitivity Analysis
44. · 1. The Concept of Residual Confusion
45. · 2. The E-Value (E-Value)
46. Reporting, Recording and Good Practices Guidelines
47. · 1. International Reporting Guidelines (Enhancing the Quality and Transparency of Health Research)
48. · 2. The Prior Registration of Protocols