The course delivers core components of identifying, combining and analysing real-world data to support health care decision making. It covers aspects of data management, data manipulation and advanced methods of data analysis. These include methods of comparative effectiveness research using observational data, advanced survival analysis techniques, and methods to address issues around missing data. The module is aimed at health, social and clinical researchers, who wish to learn techniques and skills to analyse real-world data.

10 week online course comprising lectures and practical exercises.

None

Essay of 1,000 words (25%) (ILOs 1-3)

Coursework focussing on data manipulation and analysis skills as well as interpretation of results (75%). Students will be given datasets for which they have to answer a number of research questions (ILOs 4-6), including data manipulation and cleaning and regression analysis. Students will have to prepare a report, presenting their analysis steps and results together with an interpretation of results in reply to the research questions.

This aim of this course is to teach students the principles of using real-world data (observational data) for health care decision making. This includes theoretical concepts as well as practical skills. It further aims to equip students with the necessary analytical skills to analyse large real-world data sets that often come from different sources, requiring linkage.

On completion of the course the student will be able to:

1. 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

2. Critically assess sources of bias and measurement error in routinely collected health data.

3. Contrast and critically discuss advantages and disadvantages of different study designs (observational data vs data collected from randomised controlled trials) in evaluating health outcomes. 

4. Evaluate and carry out necessary data manipulation steps after assessing data quality, including missing data imputation. 

5. Utilise advanced methods to adjust for confounding in comparative effectiveness analysis when using observational data.

6. Create, interpret and critically discuss output generated from basic and advanced regression analysis for individual-level data and aggregate data.

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.