Clinical Research: Applied Biostatistics — Treatise on Methodology and Scientific Analysis

Research Methodology · Clinical Bases

How it begins

Un manual de referencia para investigadores y clínicos: desde los axiomas de la probabilidad hasta la inferencia causal, el análisis de supervivencia avanzado y los modelos predictivos de aprendizaje automático.

What it covers

1. Applied Biostatistics
2. Probability Theory and Typology of Clinical Variables
3. · Typology of Variables in Medical Research
4. · Clinically Relevant Probability Distributions
5. Clinical Study Design, Control of Bias and Confusion
6. · Exhaustive Taxonomy of Research Designs
7. · Control of Confusion and Biases
8. Descriptive Statistics and Scientific Inference
9. · Measures of Central Tendency and Dispersion
10. · The Central Limit Theorem (CLT)
11. · Confidence Interval Estimation Theory
12. Parametric Hypothesis Testing
13. · The Student t Test Family
14. · Analysis of Variance (ANOVA)
15. Non-Parametric Hypothesis Testing
16. · Equivalent Taxonomy of Statistical Tests
17. · Analysis of Categorical Data and Contingency
18. Multivariate Regression Models
19. · 1. Multiple Linear Regression
20. · 2. Multiple Logistic Regression
21. Survival Analysis and Time-Event Modeling
22. · The Kaplan-Meier Estimator
23. · Comparison of Survival Curves: The Log-Rank Test
24. · The Cox Proportional Hazards Model
25. Calculation of Sample Size and Statistical Power
26. · Parameters Required to Estimate Sample Size
27. · Mathematical Formulas for Sample Sizing
28. Analysis of Randomized Controlled Clinical Trials
29. · Analysis Strategies in Clinical Trials
30. · Scientific Treatment of Lost Data (Missing Data)
31. Analysis of Diagnostic Tests and ROC Curves
32. · Diagnostic Performance Parameter Formulation
33. · Likelihood Ratios (LR)
34. · ROC Curve Analysis (Receiver Operating Characteristic)
35. Multiplicity, Subgroup Analysis and Publication Bias
36. · Approaches to Multiplicity Adjustment
37. · Detection and Correction of Publication Bias
38. Meta-analysis and Quantitative Synthesis of Evidence
39. · Statistical Models in Meta-analysis
40. · Evaluation and Measurement of Heterogeneity
41. Advanced Clinical Trial Designs
42. · Adaptive Clinical Trial Designs
43. · The Revolution of Bayesian Inference in Medicine
44. Causal Inference, Big Data and Machine Learning
45. · Key Causal Inference Techniques
46. · Introduction to Machine Learning in Medicine