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In this module you will build on your understanding from the Applied Epidemiology and Statistics for Public Health modules of the MPH. You will develop further your knowledge and skills of quantitative research methods relevant to public health data analysis and public health intelligence. You will use Stata statistical software to build on skills developed earlier in the programme and be introduced to R, and signposted to other relevant software packages (e.g. Python).Ìý

You will learn about alternative study designs (e.g. quasi-experimental studies, big data and randomised controlled trials), the wide range of data sources relevant for public health, and about emerging technologies such as machine learning and Bayesian analysis, and their potential influence in public health. You will learn how these approaches are important as computing resources and large administrative health datasets become more available, presenting both challenges and opportunities for public health research. You will take a critical view of the whole research pathway from data sourcing to dissemination.Ìý

You will have the opportunity to develop skills in:-Ìý

• data management (e.g. cleaning, coding, open access, missing data)Ìý

• data linkageÌý

• additional statistical analysis methods (e.g. time series, survival analysis and multilevel modelling)Ìý

• developing a quantitative research protocol and analysis planÌý

• interpretation of complex health, care and population dataÌý

• presentation of data to technical and non-technical audiences (e.g. good graphs and tables, visualisation tools, and data dashboards)Ìý

• communication of quantitative findings to technical and non-technical audiencesÌý

• causal inferenceÌýÌý

You will learn about when to use these different methods, and the key strengths and limitations of them. You will be challenged to apply these methods in real-world public health scenarios.Ìý

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The aim of this module is to provide students with a deeper understanding of quantitative methods and their relevance to public health. The module will build on methods covered in the Applied Epidemiology and Statistics for Public Health modules and introduce students to alternative study designs, a wider selection of statistical techniques and approaches for interpretation, presentation, and communication of complex health data to technical and non-technical audiences.Ìý