Clinical Epidemiology — Validity, Errors and Biases

Public Health and Epidemiology · Clinical Bases

How it begins

An exhaustive, expert-level treatise on the logical, mathematical, and methodological bases that support epidemiological inference. From the molecular dissection of random and systematic error to the most advanced analytical strategies to neutralize confusion and bias in the era of Big Data and Evidence-Based Medicine.

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. Confounding 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 Confounding Bias in the Design Phase
18. · 1. Randomization
19. · 2. Restriction
20. · 3. Pairing or Coupling (Matching)
21. Control of Confounding 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 Sensation Analysis
44. · 1. The Concept of Residual Confounding
45. · 2. The 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