Appendices
E
References
Advanced Statistics and Machine Learning for Health Research
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1
Advanced Statistics and Machine Learning for Health Research
Pre-Course: Foundations
2
Setting Up Your Computing Environment
3
Probability, Distributions, and the Language of Uncertainty
4
Statistical Inference: What We Can and Cannot Conclude
5
Regression: The Workhorse of Clinical Research
Advanced Statistical Methods
6
Modelling Non-Linear Relationships: Splines, Fractional Polynomials, and Why Categorisation Fails
7
Penalised Regression: Ridge, LASSO, and Elastic Net
8
Survival Analysis: Time-to-Event Data in Medicine
Bayesian Methods
9
Bayesian Inference: A Different Way to Think About Evidence
10
Applied Bayesian Modelling: Regression, Hierarchical Models, and Clinical Applications
Introduction to Machine Learning
11
What Is Machine Learning? Core Concepts for Clinical Researchers
12
Decision Trees, Random Forests, and Gradient Boosting
13
Introduction to Neural Networks
14
Evaluating Models: Beyond Accuracy
Dimensionality Reduction and Unsupervised Learning
15
PCA, t-SNE, and UMAP: Making Sense of High-Dimensional Data
16
Clustering: Discovering Patient Subgroups
Clinical Prediction Models
17
Developing Clinical Prediction Models: A Complete Workflow
18
Performance Assessment and Model Validation
19
Reporting Standards, TRIPOD+AI, and Clinical Impact
Advanced Research Toolkit
20
Causal Inference for Observational Health Data
21
Meta-Analysis Methods for Evidence Synthesis
22
Producing Journal-Ready Statistical Analyses
Appendices
A
Dataset Codebook
B
Mathematical Notation Reference
C
Further Reading and Resources
D
Exercise Solutions
E
References
Appendices
E
References
Appendix E — References
Code
D
Exercise Solutions
Source Code
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