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MATH4407: Clinical Trials

Please ensure you check the module availability box for each module outline, as not all modules will run in each academic year. Each module description relates to the year indicated in the module availability box, and this may change from year to year, due to, for example: changing staff expertise, disciplinary developments, the requirements of external bodies and partners, and student feedback. Current modules are subject to change in light of the ongoing disruption caused by Covid-19.

Type Open
Level 4
Credits 10
Availability Available in 2023/24
Module Cap None.
Location Durham
Department Mathematical Sciences

Prerequisites

  • Advanced Statistical Modelling (MATH3411)

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 - cluster RCT, adaptive RCT.
  • Statistical analysis for different data types (eg. binary, proportions, continuous, survival).
  • Bayesian methods for RCTs
  • 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 the Bayesian approach to 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.
  • Practical classes demonstrate how to implement the techniques studied, consolidate the studied material, and enhance practical understanding.
  • 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

ActivityNumberFrequencyDurationTotalMonitored
Lectures202 per week for 10 weeks1 hour20 
Practical classes21 in week 4, 1 in week 92 hours4 

Summative Assessment

Component: CourseworkComponent Weighting: 100%
ElementLength / DurationElement WeightingResit 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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