Experimental and Observational Methods for Health Data Science

Delivered in partnership between the University of Birmingham's Institute for Interdisciplinary Data Science and AI and Health Data Research UK (HDR UK), this course is part of a series intended to enable graduates with advanced skills in mathematics and computational science to gain insight into key topics in health data science.

If you are considering a career in health data science, have recently transferred from another discipline or are already working in health data science but want to widen your knowledge, our courses are designed to provide a rapid introduction to key areas.

Location: University of Birmingham, Edgbaston, UK (please note this is an in-person event ONLY)
Dates: Monday 30th January-Friday 3rd February 2023 inclusive
Fee: £200
Applications: Opening end November 2022 - Register your interest below!

What are the aims of this course?

Appropriate health study design is critical to ensure that we can answer the questions we are interested in, for example, is a drug effective for treating a condition? Does daily monitoring of your blood pressure lead to reduced chance of a heart attack? However, health studies often need to consider a number of complex factors which might lead to biased or misleading results.

This course aims to give participants an insight into how health studies are designed and implemented. Using a combination of lectures, seminars, and interactive, group-based problem-solving sessions, you will learn about key aspects of health study design. You will also hear from academic and industry-based experts as to how they implement studies in the real-world.

What will I learn? 

Upon completion of this course, you will:

  • Have developed an understanding of how health studies are conducted
  • Have understood the difference between interventional and observational health studies
  • Be able to identify some suitable study designs for specific health problems
  • Have understood how study design impacts the potential uses of health data
  • Have been introduced to the utility of genetics in health studies
  • Have applied statistical techniques to account for forms of bias in observational studies
  • Be able to design simple interventional studies to test for a particular hypothesis

How will the course be delivered?

Each day will focus on a particular topic:

  • The scientific method in health sciences
  • Randomised controlled trials and interventional data
  • Mendelian randomisation and instrumental variables
  • Observational studies and dealing with bias
  • Designing an experimental study

Each morning will consist of lessons and activities that test your existing knowledge and then introduce the essential concepts, followed by a talk from a guest speaker that illustrates a real-world example.

Each afternoon will consist of a practical computational activity that will allow you to put the knowledge you have learned that day into practice.

What academic background/experience do I need to take part?

To take part, no prior health data science experience is required, but you will need to:

  • Have a first degree in a quantitative discipline
  • Be familiar with programming in Python or R (or be familiar with programming in general and have the ability and willingness to learn Python or R)

What is included? 

Dinner will be provided on the first evening, and a light lunch and refreshments all week, but you will be expected to arrange and fund all other meals, travel and accommodation (a list of suitable accommodation will be provided after booking).

Are there any stipends to cover costs?

A small number of stipends of up to £500 will be available to cover the registration fee, travel and/or accommodation.
Further details and information on how to apply for a stipend will be available once applications open.

If you have any queries about the immersion weeks, please email dsai@contacts.bham.ac.uk

Register your interest now

  • Name
  • I would like to register my interest in attending this Health Data Immersion Week, and consent to being contacted when applications open.
  • To know how the University uses your data, please read our privacy notice. Mailing List privacy notice, https://www.birmingham.ac.uk/privacy/mailing-lists.aspx

 

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