MSc Health Data Science

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.

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. 

The unit will cover the following topics: 
Section 1: Modelling complex data

• Survival analysis: Kaplan Meier; Cox proportional hazards; time-updated variables/ non-proportional hazards 
• Hierarchical Linear Models / multi-level models: longitudinal data analysis
• Developing, validating, implementing and evaluating prediction models in practice

Section 2: Causal Inference

• 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 
   

The unit will be taught in a blended-learning format: basic knowledge and directed reading will be provided via eLearning so as to introduce students with key knowledge.  The face-to-face time will consist of a series of lectures and discussions in which core concepts (introduced through pre-reading) 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, lectures will be recorded and distributed online.  Alongside this, two designated tutorials will be made available for students with academic staff as well as on-going support both online and F2F. 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. 

• 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. 
   

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

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

• 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

Summative assignments include one long report at 1500 words counting for 80%, and one short report at 750 words counting for 20%.

Each assignment should include statistical scripts demonstrating the analysis of data and a written report (in paper style) to justify methods and explanation of work.

Formative assignments will be delivered through a wide range of interactive exercises and supervised practical exercises. 

Feedback on summative assignments will be delivered via Blackboard within 15 working days with drop-in sessions held for further feedback discussions.

Verbal feedback will be delivered real-time. 

Kirkwood, B. R., & Sterne, J. A. (2010). Essential medical statistics. John Wiley & Sons.

Hernán MA, Robins JM (2024). Causal Inference: What If. Boca Raton: Chapman & Hall/CRC. (Parts I and II only)