Applied Statistical Modelling for Health and Life Sciences CPD Course
Dates: 8th, 13th, 15th, 20th and 22nd May 2025 (13:00 - 17:00 UK Time)
Venue: Synchronous via Zoom
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Early Bird Rate available until 20th March!
Photo credit: Tara Winstead
About the course
This course provides a broad introduction to statistical modelling for health and life science applications. The fundamental role of the statistician as a problem solver will be emphasised and the different stages of the “problem solving” cycle will be considered.
The course will equip you with the theoretical underpinning and computational skills needed for generalized linear regression modelling of health data using the R statistical software. Real-world case studies drawing on our world-class epidemiological research will be used to help you develop an appreciation of modelling strategy and to give you practical experience of interpreting model findings in the context of real health problems.
Course requirements
The course requires familiarity with basic statistical concepts and methods (e.g. descriptive statistics and graphs, t-tests). Some knowledge of R would be advantageous. On 8th May the course has built in a half-day preparatory session on R software to support all delegates in achieving the required level of proficiency in the R software environment.
Learning outcomes
- Formulate health research questions as statistical problems.
- Learn and be able to implement linear regression, logistic regression and survival analysis methods in R software.
- Interpret and critically evaluate the results of statistical modelling in the context of a quantitative research question.
After completing this course, you might be interested in exploring the Master in Health Data Science to develop advanced understanding and skills in statistical computing, machine learning and analysis of big data.
External accreditation
CPD points applied for through the Royal College of Physicians.
BOOK NOW!
Early Bird Delegate Rate - £1,170 (until 20th March 2025)
Standard Delegate Rate - £1,300 (from 21st March 2025)
Reduction for PhD students/UoE Staff - £800
Delivery dates:
8th, 13th, 15th, 20th & 22nd May 2025 (13:00-17:00 GMT)
Delivery format
Synchronous delivery via Zoom.
Suitable for Academics, PhDs, industry/NHS, and for those who need to use quantitative models to analyse data to generate evidence or inform policy. This includes researchers and data analysts wanting a practical framework for modern regression modelling of data from clinical trials, or routine health data from electronic health records.
Programme
(*The programme may be subject to minor amendments)
We advise you to make every effort to join all sessions to gain the most out of the course.
Day 1- Pre-course – Introduction to R - 8th May 2025
Using R Studio to write scripts and run R commands
- Different types of objects for storing data
- Reading in and organising data, and saving the output
- Summarising and visualising data in R
Day 2 - Linear Regression Modelling - 13th May 2025
FORMAT |
TOPIC |
DURATION |
Lecture I |
What is statistical modelling |
30 minutes |
Lecture II |
An introduction to linear regression analysis: model estimation and interpretation |
30 minutes |
Lecture III |
Model assumptions and diagnostics |
30 minutes |
Break |
30 minutes |
|
Workshop I |
Case study: the Brains for Dementia Research Study |
15 minutes |
Workshop II |
Linear regression analysis in R |
60 minutes |
|
Break |
15 minutes |
Workshop III |
Interactive discussion and wrapping up |
30 minutes |
Day 3 – Logistic Regression Modelling – 15th May 2025
FORMAT |
TOPIC |
DURATION |
Lecture I |
Introduction to statistical modelling of categorical data |
30 minutes |
Lecture II |
Logistic regression analysis: model estimation and interpretation |
30 minutes |
Lecture III |
Model assumptions and diagnostics |
30 minutes |
Break |
30 minutes |
|
Workshop I |
Case study: the Diabetes prediction dataset |
15 minutes |
Workshop II |
Logistic regression analysis in R |
60 minutes |
|
Break |
15 minutes |
Workshop III |
Interactive discussion and wrapping up |
30 minutes |
Day 4 - Complex Data Structures and Interaction Modelling – 20th May 2025
FORMAT |
TOPIC |
DURATION |
Lecture I |
Complex data structures in health research |
15 minutes |
Lecture II |
Modelling statistical interactions |
30 minutes |
Lecture III |
An introduction to hierarchical regression models |
45 minutes |
Break |
30 minutes |
|
Workshop I |
Case study: longitudinal cognitive and clinical assessment data from the Brains for Dementia Research Study |
15 minutes |
Workshop II |
Mixed effects models in R |
60 minutes |
|
Break |
15 minutes |
Workshop III |
Interactive discussion and wrapping up |
30 minutes |
Day 5 – Survival Analysis - 22nd May 2025
FORMAT |
TOPIC |
DURATION |
Lecture I |
Time-to-event data in health research |
30 minutes |
Lecture II |
An introduction to Cox regression analysis: model estimation and interpretation |
30 minutes |
Lecture III |
Model assumptions and diagnostics |
30 minutes |
Break |
30 minutes |
|
Workshop I |
Case study: the Framingham Heart Study |
15 minutes |
Workshop II |
Survival analysis in R |
60 minutes |
|
Break |
15 minutes |
Workshop III |
Interactive discussion and wrapping up |
30 minutes |
Whether you study on-campus, virtually, or via a blended learning programme, you'll be supported by the University of Exeter's world-class faculty with wide-ranging opportunities to develop interdisciplinary and vocational skills.
Learn about our Master of Health Data Science.
Speakers
Led by University of Exeter Professor William Henley and guest lecturer, Dr Adam Streeter (Münster, Germany) alongside a team of invited experts.