Tutorials & Courses

Teaching material maintained by CTIR. Source repositories: CTTIR/tutorials and CTTIR/courses. Material is published under the MIT license, matching the upstream repositories.

Courses Link to heading

A four-part biostatistics curriculum, taught with R. Site: https://cttir.github.io/courses/.

Course 1— Foundations of Biostatistics with R: the scientific process, data hygiene, probability, sampling, and the core hypothesis tests that anchor everything else.
Course 2— Regression, ANOVA & Model Diagnostics: linear models, ANOVA, GLMs, diagnostics, calibration, and honest model evaluation.
Course 3— Study Design, Longitudinal Data & Causal Inference: designing studies; handling missing, clustered, and time-to-event data; and making causal claims with care.
Course 4— Modern Statistical Learning & High-Dimensional Biomedicine: regularisation, tree ensembles, Bayesian modelling, omics, and reproducibility at scale.

Tutorials Link to heading

A topic-organised library of standalone biostatistics tutorials in R. Site: https://cttir.github.io/tutorials/.

Statistical Foundations— Foundational concepts: probability, sampling, inference framework.
Descriptive Statistics— Summarising and exploring data before modelling.
Probability Theory— Axioms, distributions, expectations, and the algebra of random variables.
Inferential Statistics— Hypothesis tests, confidence intervals, and the logic of inference.
Sample Size & Power— Planning studies with adequate statistical power.
Data Visualisation— Publication-quality graphics with ggplot2 and friends.
Regression & Modelling— Linear models, GLMs, mixed models, and beyond.
Multivariate Methods— PCA, clustering, factor analysis, discriminant analysis.
Time-Series Analysis— ARIMA, state-space, spectral, and forecasting methods.
Bayesian Statistics— Bayesian inference, MCMC, and probabilistic programming.
Survival Analysis— Censored time-to-event data: Kaplan-Meier, Cox, AFT, competing risks.
Bioinformatics— Genomics pipelines from FASTQ to differential expression and beyond.
Machine Learning— Tree ensembles, neural networks, calibration, interpretability.
Clinical Biostatistics— RCT design, adaptive trials, diagnostic accuracy, agreement.
Meta-Analysis— Effect-size pooling, heterogeneity, bias, network meta-analysis.
Experimental Design— Factorials, response surfaces, mixture designs, robust design.