MSc Health Data Science

Year of entry: 2022

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Course unit details:
Statistical Modelling and Inference for Health

Unit code IIDS67642
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 2
Offered by Division of Informatics, Imaging and Data Sciences
Available as a free choice unit? No

Overview

The health sector is rich with data that currently remains under-utilised and often uses data to look at past healthcare delivery rather than using the data in order to provide insight to enhance healthcare delivery. 

A key component skill set of a data scientist is to be able to understand and implement a suite of predictive modelling and data mining methods in order to this. 

This unit will be build on central concepts and methods introduced in the pre-requisite unit ’Fundamentals in Mathematics and Statistics in Health Data Science’ in order to provide a complete data analytical toolkit (including machine learning methods) to explore health data.

The unit will be application driven with case-studies and examples will be drawn from health research across the University of Manchester and Health eResearch Centre.

Pre/co-requisites

Unit title Unit code Requirement type Description
Fundamental Mathematics & Statistics for Health Data IIDS67631 Pre-Requisite Compulsory

Aims

This unit aims to build on the skills introduced in the unit ‘Fundamentals in Mathematics and Statistics for Health Data Science’ to provide students with an understanding of forecasting, and more complex predictive modelling and data mining methods that allow a deeper insight into bio-health data.

The unit will develop students literacy in the strengths, characteristics and correct application of modelling techniques, and how to interpret results.

On completion students will also be able to implement analyses in an appropriate scripting language.

In addition, the unit will develop students ability to critically appraise literature that describe previously implemented methods to address healthcare problems. 

Learning outcomes

The unit will cover the following topics:

Section 1: Modelling complex data (3 days)

  • Survival analysis: Kaplan Meier; Cox proportional hazards (including cause-specific and competing risk); time-updated variables/ non-proportional hazards
  • Hierarchical Linear Models / multi-level models: longitudinal data analysis; time series analysis; joint longitudinal models
  • Developing, validating, implementing and evaluating prediction models in practice

Section 2: Causal Inference (3days)

  • Confounding, selection bias and measurement error.
  • Directed acyclic graphs, d-separation
  • Study design principles
  • Adjustment methods: matching, stratification and inverse matching, propensity scores
  • Missing Data

Teaching and learning methods

The course will be taught in a blended-learning flipped classroom format: basic knowledge, directed reading, video lectures, and other interactive activities will be provided via eLearning so as to introduce students with key knowledge.  The face-to-face time will focus on group work, tutorials and practical workshops. As required, core concepts will be re-capped and any further development discussed, as well as supervised computer time during which practical software and programming problems will be explored.  When possible, sessions will be recorded and distributed online.  The F2F time will be delivered as weekly tutorials. Associated with each key concept will be a practical exercise to assess the understanding of the students.  The unit assessment will require the student to write scripts to demonstrate one or more of the techniques covered in the unit and a written report describing the justification of method; key findings and working.  There will be online tutor support for the eLearning preparation and designated (virtual/F2F) office hours will be made available for students with academic staff to ensure that students are continuing to progress through the unit.

Knowledge and understanding

  • Explain and discuss modelling and causal inference techniques and appraise their application in healthcare
  • Appraise the strengths and weakness of modelling methods
  • Discuss the challenge of causal inference and the strong assumptions required.

Intellectual skills

  • Critically appraise literature that uses mathematical/statistical methods for health data.
  • Assess the effectiveness and fitness fpor purpose of modelling a tool or technique.
  • Apply modelling techniques and methods to health data.
  • Interpret analytical results.

Practical skills

  • Design and write scripts to implement statistical/mathematical methods to analyse health data.

Transferable skills and personal qualities

  • Communicate and write analytical methods based on completed work and available literature in this area
  • Solve problems
  • Understand technical descriptions of statistical/mathematical analysis methods

Employability skills

Analytical skills
Understand technical descriptions of statistical/mathematical analysis methods
Problem solving
Solve problems

Assessment methods

2 x Data Analysis/Programming Assignments with written report

Feedback methods

Formative assessment and feedback to students is a key feature of the on-line learning materials for this unit.  Students will be required to engage in a wide range of interactive exercises to enhance their learning and test their developing knowledge and skills. 

In addition, there will be a series of supervised practical hands-on exercises that will allow for verbal feedback. 

Study hours

Scheduled activity hours
eAssessment 30
Lectures 18
Practical classes & workshops 24
Tutorials 2
Independent study hours
Independent study 76

Teaching staff

Staff member Role
Glen Martin Unit coordinator
Nophar Geifman Unit coordinator

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