HAR6170: Medical Statistics and Evidence Synthesis

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Overview

The Medical Statistics and Evidence Synthesis module is led by Jessica Forsyth. It runs in the Autumn semester and is worth 15 credits.

It is one of the modules on:

This module is available as a CPD option

This module is available as a DDP module

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Introduction

This module introduces students to key concepts and methods used in medical statistics.

The module is organised in two parts:

1. the design, analysis and reporting of randomised control trials
2. the synthesis of evidence from multiple clinical trials using meta-analysis methods and quantifying uncertainty using experts’ beliefs

Classical (ie frequentist) and Bayesian meta-analysis methods will be presented with an emphasis on how evidence can be used to represent uncertainty about input parameters in decision analytical models.

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Objectives

This module aims to:

• Describe the main Classical (ie frequentist) statistical methods used in medical applications.
• Describe the Bayesian approach to statistical inference.
• Describe how evidence from multiple clinical trials can be synthesised.
• Describe the role of experts’ beliefs in the absence of empirical evidence and methods of elicitation.

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Learning outcomes

On satisfactory completion of the course, a student will be able to:

1. Describe the process of setting and testing statistical hypotheses in randomised controlled trials
2. Describe and apply appropriate Classical statistical methods for analysing different types of data collected in randomised controlled trials
3. Conduct Classical analyses of data collected in randomised controlled trials using statistical software
4. Describe the Bayesian approach to statistics and contrast it with the Classical approach
5. Describe what is meant by fixed effect and random effects models for meta-analysing evidence from multiple randomised controlled trials
6. Describe what is meant by a standard pairwise meta-analysis and a network meta-analysis of evidence from multiple randomised controlled trials, and be able to conduct a Bayesian analysis using appropriate statistical software
7. Describe the process of the elicitation of experts’ beliefs

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Teaching methods

Informal lectures, some of which will include exercises for students to complete, will provide the majority of contact hours with lecturers (10 x 2 hours). For nine weeks, tutorials will be given to undertake computer-based problem solving (10 x 2 hour).

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Expectations

Students will be expected to undertake approximately 110 hours of independent study, including preparation for tutorials, preparation for assignments and further recommended reading of articles published in peer review journals and text books.

These learning methods will reinforce learning outcomes and provide practical experience of applying some concepts.