Levels and recommended path
Begin with Foundation material, continue to Applied methods that match your research questions, then use Advanced topics when your design or analysis requires greater depth.
- Start with Parts I–IV for concepts, data, description, and inference.
- Use Part V to connect the research question to an analysis.
- Continue to the outcome- and design-specific parts most relevant to your work.
- Finish with planning, reporting, appraisal, and specialised topics.
Parts I–XVI
Curriculum
130 published of 130 planned lectures
Part 1
Foundations of Biostatistics
8 modules
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- Why Biostatistics Matters in Medicine
- From Clinical Question to Statistical Question
- Understanding Variables and Data Types
- Scales of Measurement
- Population, Sample and Sampling
- Study Designs and Their Statistical Implications
- Introduction to Bias, Confounding and Effect Modification
- Planning a Statistical Analysis
Part 2
Data Management and Exploration
6 modules
Part 3
Descriptive Statistics
6 modules
Part 4
Statistical Inference
10 modules
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- Sampling Variability and Standard Error
- Confidence Intervals
- Hypothesis Testing
- Understanding the P-value
- Statistical Significance versus Clinical Importance
- Effect Size Measures
- Type I Error, Type II Error and Statistical Power
- One-Sided and Two-Sided Tests
- Multiple Comparisons and Subgroup Analyses
- Parametric and Non-Parametric Methods
Part 5
Selecting the Appropriate Statistical Analysis
1 module
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Part 6
Analysis of Numerical Outcomes
10 modules
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Part 7
Analysis of Categorical Outcomes
7 modules
Part 8
Correlation and Regression
12 modules
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- Correlation Analysis
- Simple Linear Regression
- Multiple Linear Regression
- Building a Multivariable Model
- Interactions and Effect Modification
- Non-Linearity in Regression
- Regression Diagnostics
- Binary Logistic Regression
- Multinomial Logistic Regression
- Ordinal Logistic Regression
- Poisson and Negative Binomial Regression
- Prediction Models in Medicine
Part 9
Repeated, Clustered and Longitudinal Data
6 modules
Part 10
Survival and Time-to-Event Analysis
6 modules
Part 11
Diagnostic and Screening Studies
9 modules
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- Understanding the Diagnostic 2 × 2 Table
- Sensitivity and Specificity
- Predictive Values
- Likelihood Ratios and Diagnostic Odds Ratio
- Receiver Operating Characteristic Curves
- Selecting Diagnostic Cut-Off Values
- Comparing Diagnostic Tests
- Diagnostic Accuracy with Imperfect Reference Standards
- Screening Programme Measures
Part 12
Reliability, Agreement and Measurement
8 modules
Part 13
Clinical Trials
9 modules
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- Statistical Principles of Randomised Controlled Trials
- Baseline Characteristics in Clinical Trials
- Intention-to-Treat and Per-Protocol Analyses
- Analysis of Continuous and Binary Trial Outcomes
- Analysis of Repeated Trial Outcomes
- Superiority, Non-Inferiority and Equivalence Trials
- Cluster-Randomised and Crossover Trials
- Adverse Events and Safety Outcomes
- Interim Analyses and Data Monitoring
Part 14
Sample Size and Study Planning
9 modules
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- Principles of Sample Size Determination
- Sample Size for Estimating Means and Proportions
- Sample Size for Comparing Groups
- Sample Size for Correlation and Regression
- Sample Size for Diagnostic Studies
- Sample Size for Survival Studies
- Adjustments to Sample Size
- Choosing a Clinically Meaningful Effect Size
- Common Errors in Sample Size Calculation
Part 15
Interpretation, Reporting and Critical Appraisal
9 modules
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- How to Read a Statistical Results Table
- How to Interpret Statistical Software Output
- Writing the Statistical Methods Section
- Writing the Statistical Results Section
- Presenting Multivariable Analysis
- Common Statistical Errors in Medical Papers
- Critical Appraisal of Statistical Methods
- Reporting Guidelines for Medical Research
- Reproducibility and Transparent Analysis
Part 16
Optional Advanced or Specialised Topics
14 modules
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- Propensity Score Methods
- Causal Diagrams and Directed Acyclic Graphs
- Mediation Analysis
- Interrupted Time-Series Analysis
- Joinpoint and Trend Analysis
- Meta-Analysis and Forest Plots
- Network Meta-Analysis Overview
- Bayesian Statistics for Clinicians
- Machine Learning versus Traditional Statistical Modelling
- Handling High-Dimensional Medical Data
- Latent-Variable Analysis and Structural Equation Modelling
- Cost-Effectiveness and Health-Economic Analysis
- Spatial and Geographical Health Data
- Analysis of Complex Survey Data
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Use note
These materials support learning and research planning. They do not replace statistical consultation for a specific study.