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MATH4407: Clinical Trials

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Type	Open
Level	4
Credits	10
Availability	Available in 2024/2025
Module Cap	None.
Location	Durham
Department	Mathematical Sciences

Prerequisites

Statistical Inference II (MATH2711) AND Statistical Modelling II (MATH2697).

Corequisites

None

Excluded Combinations 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
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.

More information

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