Undergraduate Programme and Module Handbook 2024-2025
Module MATH4407: Clinical Trials
Department: Mathematical Sciences
MATH4407: Clinical Trials
| Type | Open | Level | 4 | Credits | 10 | Availability | Available in 2024/2025 | Module Cap | None. | Location | Durham |
|---|
Prerequisites
- Statistical Inference II (MATH2711) AND Statistical Modelling II (MATH2697).
Corequisites
- None
Excluded Combination of Modules
- None
Aims
- To introduce randomised controlled trials (RCTs) as the 'gold standard' of studying causal relationships.
- To investigate issues around the design, planning and analysis of RCTs.
- To develop several statistical methods for the analysis of RCT data.
Content
- Issues in designing an RCT.
- Different types of RCT (e.g. cluster RCT, adaptive RCT, Bayesian methods for RCTs).
- Statistical analysis for different data types (eg. binary, proportions, continuous, survival).
- Some practical issues, for example the phases of a clinical trial for a drug, ethics.
Learning Outcomes
Subject-specific Knowledge:
- An understanding of how an RCT is designed, implemented and analysed.
- An appreciation of issues around RCTs such as reproducibility.
- A knowledge of analysis methods for different RCT data types (e.g. binary, proportions, continuous, survival).
- An understanding of some more advanced aspects of RCTs.
Subject-specific Skills:
- In addition students will have specialised statistical skills in the following areas which can be used with minimal guidance: design, analysis.
Key Skills:
- Problem-solving, critical and analytical thinking, communicating scientific results via report-writing.
Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module
- Lectures demonstrate what is required to be learned and the application of the theory to practical examples.
- Computer practicals consolidate the studied material, explore theoretical ideas in practice, enhance practical understanding, and develop practical data analysis skills.
- Assignments for self-study develop problem-solving skills and enable students to test and develop their knowledge and understanding.
- Coursework gives students the opportunity to apply their knowledge to a given situation, and to demonstrate their understanding.
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total/Hours | |
|---|---|---|---|---|---|
| Lectures | 20 | 2 per week for 10 weeks | 1 hour | 20 | |
| Computer practicals | 5 | 1 per week in weeks 13, 15, 17, 19 | 1 hours | 5 | |
| Preparation and reading | 75 |
Summative Assessment
| Component: Coursework | Component Weighting: 100% | ||
|---|---|---|---|
| Element | Length / duration | Element Weighting | Resit Opportunity |
| Assignment 1 | 50% | ||
| Assignment 2 | 50% | ||
Formative Assessment:
Regular assignments to be formatively assessed and returned with feedback. Other problems are set for self-study and complete solutions are made available to students.
■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University