Data Analysis for Precision Health
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Overview
As we enter the data revolution, the scale and accessibility of health and medical data has reached unprecedented levels creating a growing need for expertise in extracting insights from this data.
This course will provide participants with essential statistical skills to analyse and interpret health and medical data. Key topics include linear models, mixed effect models, logistic regression and survival analysis.
Real health and medical data will be utilised to explore common challenges, practical workarounds, and translate data into actionable insights.
What you'll learn
By the end of this course, you will be able to:
- formulate and interpret appropriate linear models to describe the relationships between multiple factors
- train and evaluate logistic regression models for binary data
- understand and apply linear mixed effect models for data with repeated measures
- visualise survival data with Kaplan-Meier curves and perform inference with Cox proportional hazards models.
Sydney Precision Data Science Centre
Aims
This course will give participants the necessary understanding and skills to perform statistical analyses on health and medical data.
Participants will gain experience in working with real data and develop critical thinking skills to address common challenges, such as missing data.
Participants will learn to communicate their data and findings through graphical and statistical summaries.
Content
- Linear models: stepwise model selection, LASSO regression, model visualisation, prediction intervals
- Mixed effect models: repeated measures
- Logistic regression: odds, model evaluation, model stability
- Survival analysis: Kaplan-Meier curve, Cox proportional hazards model, C-index
The course covers four main topics:
The content is designed for medical doctors, nurses, clinicians, epidemiologists, public health researchers, and students who want to develop their data analysis, report writing, and reviewing skills.
The course will be delivered over one and half days where four overarching concepts will be covered by a combination of lectures and labs.
The course will require participants to bring their own device and will include a short pre-course module to ensure all participants have revised key concepts.
This pre-work will include ensuring their software is up to date and ready to commence the practical component on day one.
Materials
All course materials will be provided electronically to students.
Software
Please ensure R is installed on your device before class. This can be downloaded from https://cran.r-project.org/. You may also want to have an integrated developer environment installed, for example RStudio Desktop. This can be downloaded from https://posit.co/download/rstudio-desktop/. Please ensure you have the most recent versions.
The course will require participants to bring their own device and will include a short pre-course module to ensure all participants have revised key concepts. This pre-work will include ensuring their software is up to date and ready to commence the practical component on day one.
This is an intermediate level course. Participants should have introductory knowledge of statistics and R programming. Some resources will be provided prior to the start of the course to recap the fundamental assumed knowledge.
The assessment involves submitting a written analytical report of real health or medical data. Participants must perform a range of statistical analyses and provide their interpretation of the results.
Sessions
| When | Time | Where | Session Notes |
|---|---|---|---|
| Wed 23 Apr 2025 | 1pm - 5pm (UTC+10:00) | Room TBA - Face-to-face (venue TBA) | |
| Thu 24 Apr 2025 | 9am - 5pm (UTC+10:00) | Room TBA - Face-to-face (venue TBA) |
Upcoming classes
Face-to-face (venue TBA)
| When | Time | Where | Session Notes |
|---|---|---|---|
| Wed 23 Apr 2025 | 1pm - 5pm (UTC+10:00) | Room TBA - Face-to-face (venue TBA) | |
| Thu 24 Apr 2025 | 9am - 5pm (UTC+10:00) | Room TBA - Face-to-face (venue TBA) |
Facilitators
Garth Tarr
