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

Research Methodology · Clinical Bases

How it begins

A reference manual for researchers and clinicians: from the axioms of probability to causal inference, advanced survival analysis, and predictive machine learning models.

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 Confounding 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