Real-world data in healthcare decision making
Our Data Science course aims to equip students with the necessary analytical skills to analyse linked health care data and to be aware of issues around clinical and information governance relating to their use.
- Course dates: 8th January - 22nd March 2024
- Flexible Online Distance Learning (ODL) - learn at your own pace, in your own time
- Non-accredited, but with a certificate of attendance (there is no assessment)
- This course will be taught using STATA and/or R
- Course Coordinator: Dr Claudia Geue, Senior lecturer in Health Economics and Health Technology Assessment
On completion of the course the student will be able to:
- Critically discuss the key issues of utilising real-world data in health care decision making, in particular with a view of informing decisions on the adoption and use of health technologies
- Critically assess sources of bias and measurement error in routinely collected health data
- Contrast and critically discuss advantages and disadvantages of different study designs (observational data vs data collected from randomised controlled trials) in evaluating health outcomes
- Evaluate and carry out necessary data manipulation steps after assessing data quality
- Evaluate methods to assess types of missing data and utilise imputation methods to address missingness
- Utilise advanced methods to adjust for confounding in comparative effectiveness analysis when using observational data
- Create, interpret and critically discuss output generated from basic and advanced regression analysis for individual-level data and aggregate data
- Enhance skills in writing statistical syntax for data cleaning, data manipulation, and regression analysis of real-world health data
10-week online course comprising 10 lectures and accompanying practical exercises. The lectures will be 45min/1hr in duration and exercises associated with lectures will take a notional 2 hours for the participants to complete. Each week the academic lead will be responsible for monitoring and contributing to the discussion board. The exercise solutions will be posted on Moodle, our digital learning platform, the following week.
Full details here of the Data science course.
Registration & Fees
£660 for lower-middle-income countries (LMICs)
£704 for public/academic sector delegates
£1,100 for commercial/private sector
If you have any questions about this course, please email us: email@example.com
Fill out our online registration form to register for this course.