Module manager: Samuel Relton
Email: s.d.relton@leeds.ac.uk
Taught: Semester 1 (Sep to Jan) View Timetable
Year running 2025/26
As per programme entry requirements
This module is not approved as an Elective
This module introduces students to statistical testing, generalized linear models (GLMs) and survival models, which are the foundation for analysing observational healthcare data. Various healthcare outcomes of interest will be explored using real-life datasets, including 30-day mortality, treatment costs, length of stay in hospital etc. from NHS England data and other sources. The module will also convey best practice in model evaluation and validation, based on the TRIPOD and STAR-D guidelines for reporting of statistical models in medical journals.
The purpose of this module is to:
- Introduce students to statistical tests used throughout healthcare research
- Give students an understanding of the various statistical models commonly employed for binary, continuous, and survival outcomes
- Equip students with the ability to utilise these models on real datasets using the programming languages such as R.
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:
1. Apply basic statistical tests to compare measurements from different groups (e.g. t-test, Mann-Whitney U-test, Chi-squared test).
2. Describe the use of generalized linear models and survival models for predictive analytics and critically evaluate published research using such models based on best practice guidelines.
3. Demonstrate a critical understanding of the biases introduced by confounding variables and how to mitigate them in real-life and publicly available datasets.
4. Independently design and critically evaluate a basic statistical model, including nonlinear covariates (e.g. BMI), and interactions (e.g. age and sex).
5. Demonstrate a critical understanding by performing model selection using the Akaike Information Criterion and evaluate model performance using AUC, cross-validation, Q-Q plots, and calibration curves, for example.
6. Critically evaluate the statistical methodology used in published research and discuss the pros and cons of alternative approaches.
Skills Learning Outcomes
On successful completion of the module students will have demonstrated the following skills learning outcomes:
1. Demonstrate understanding of the impact of various working practices in the healthcare system that change the way that data is captured and recorded for research.
2. Plan a solution to address a research question using data, and consider how it may fit into a clinical workflow, appreciating any potential benefits and limitations of these changes.
3. Critically evaluate different approaches to analysing data, and consider the advantages and disadvantages of each approach.
This module includes the following concepts:
- Descriptive statistics to summarise data
- Comparison of groups
- Regression modelling of continuous, binary, and time-to-event outcomes
- Study design and robust model evaluation
- Implementing analysis plans in software
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lecture | 10 | 1 | 10 |
| Practical | 15 | 1 | 15 |
| Seminar | 5 | 1 | 5 |
| Independent online learning hours | 6 | ||
| Private study hours | 114 | ||
| Total Contact hours | 30 | ||
| Total hours (100hr per 10 credits) | 150 | ||
Lectures and seminars will be interactive, including group discussion and problem-based exercises with formative tutor feedback. There will be plenty of opportunity for rapid face-to-face feedback and clarification from the tutors during classes.
A draft outline of the written coursework will be submitted for formative assessment, so students will gain individual feedback directly applicable to their preparation for the summative work.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Coursework | Formative - Statistical analysis plan for the summative report | 0 |
| Coursework | Summative report – Design and implementation of statistical analysis (requiring regression modelling) on a real-world dataset from a previous clinical trial or similar data source (3000 words) | 100 |
| Total percentage (Assessment Coursework) | 100 | |
Students who fail the first attempt at the summative coursework will be offered an opportunity to resubmit. The resubmission will take the same format as the first attempt. LO6 will be assessed in the introduction and discussion section of the report – students are expected to compare their findings about relevant literature and think about the clinical utility of their findings, all of which require critical analysis of previous work.
The reading list is available from the Library website
Last updated: 08/05/2025
Errors, omissions, failed links etc should be notified to the Catalogue Team