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Research Design and Statistics

Module description

This module provides a broad introduction to statistical modelling for data scientists. The module starts by considering the different stages of a statistical investigation and emphasising the importance of problem formulation. The module highlights the benefits of exploratory data analysis based on descriptive statistics and graphs. You will also critically examine the concepts and models at the heart of evidence-based medicine. Embedded in this will be principles and practices  of patient and public healthcare.

Module aims - intentions of the module

By undertaking this module you will gain critical insights into the diversity of methods and core concepts needed to conduct high quality applied health research. The aims of this module are to provide skills to design and carry out research studies that not only produce valid and reliable knowledge on important health and health service problems, but research findings which are useful to those working in health or social care and health systems – whether they be health, public health or social care professionals, service or hospital managers, service commissioners or health policy makers.

The aim of the module is to provide a modern statistical framework for answering health research questions through interrogation of a variety of health datasets such as electronic health records or other observational studies. The module will equip you with the theoretical underpinning and computational skills needed for advanced regression modelling of health data. Both frequentist and Bayesian approaches to modelling will be considered and contrasted. The module will cover common statistical concepts such as bias, confounding and missing data that are relevant for real-world health applications. The module will emphasise the fundamental role of the statistician as a problem solver and consider the different stages of the “problem solving” cycle. Case studies will be used to help you develop an appreciation of modelling strategy and to give you practical experience of interpreting model findings in the context of real health problems.

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

On successfully completing the module you will be able to...

• 1. Understand the strengths and limitations of three of the main strategies of enquiry in applied health research – quantitative research, qualitative research, mixed methods research.
• 2. Critically examine the ethical risks, implications and regulations related to collecting, processing, analysing and securely storing data from people in health and care contexts, and know how these risks can be minimised or managed.
• 3. Examine and apply fundamental concepts in hypothesis testing including sampling, probability and statistical distributions.
• 4. Apply a range of statistical inference methods to address health data science problems including both simple and advanced regression models.
• 5. Apply a range of statistical inference methods to address health data science problems including both simple and advanced regression models.

ILO: Discipline-specific skills

On successfully completing the module you will be able to...

• 6. Know the common steps, choices and processes involved in designing a research study.
• 7. Have a clear understanding of the main study designs for the quantitative evaluation the outcomes of health interventions, and an appreciation of the key study design choices and elements/concepts of study design they are based on.
• 8. Formulate health research questions as statistical problems.
• 9. Draw conclusions from the results of a data analysis and justify those conclusions, appropriately acknowledging uncertainty in the results.

ILO: Personal and key skills

On successfully completing the module you will be able to...

• 10. Use the R software environment for statistical computing.
• 11. Understand and critically appraise academic research papers in research field.

Syllabus plan

• Health services and health systems (drivers and contexts, policies and guidelines)
• Evidence-based policy and practice
• Key steps, choices and considerations in designing applied health research
• Research ethics: assessing and managing risks with people and their data
• Formulating statistical problems
• Statistical computing using R
• Exploratory data analysis
• Probability theory and statistical distributions
• Hypothesis testing including parametric and non-parametric methods
• Bayesian and frequentist inference
• Power and sample size calculations
• Linear regression modelling
• Generalised linear models
• Survival analysis
• Multilevel models for longitudinal or hierarchical data
• Missing data mechanisms and multiple imputation
• Causal inference for healthcare evaluations including adjustment methods for addressing measured and unmeasured confounding

Learning activities and teaching methods (given in hours of study time)

Details of learning activities and teaching methods

Formative assessment

Summative assessment (% of credit)

Details of summative assessment

Details of re-assessment (where required by referral or deferral)

Re-assessment notes

Please refer to the TQA section on Referral/Deferral: http://as.exeter.ac.uk/academic-policy-standards/tqa-manual/aph/consequenceoffailure/  

Indicative learning resources - Basic reading

• Williams M. (2016) Key Concepts in The Philosophy of Social Research. Sage Publications, London.
• Creswell J W (2014). Research Design: Qualitative, Quantitative and Mixed methods approaches. Sage (4th Edition)
• Essential Medical Statistics. Kirkwood and Stern, Blackwell Science. (Available online: http://encore.exeter.ac.uk/iii/encore/record/C__Rb3519976 ) 
• Introductory Statistics with R, Second Edition. Dalgaard, P. Springer and Hall (2008). http://www.academia.dk/BiologiskAntropologi/Epidemiologi/PDF/Introductory_Statistics_with_R__2nd_ed.pdf
• An Introduction to Generalized Linear Models, Third Edition. Dobson, AJ and Barnett, AG, Chapman & Hall (2008). https://reneues.files.wordpress.com/2010/01/an-introduction-to-generalized-linear-models-second-edition-dobson.pdf
• An Introduction to Statistical Learning with Applications in R Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani http://faculty.marshall.usc.edu/gareth-james/ISL/ISLR%20Seventh%20Printing.pdf
• R for Data Science Garrett Grolemund, Hadley Wickham https://r4ds.had.co.nz/
• Data Analysis Using Regression and Multilevel/Hierarchical Models. Gelman and Hill, Cambridge University Press (2007). https://faculty.psau.edu.sa/filedownload/doc-12-pdf-a1997d0d31f84d13c1cdc44ac39a8f2c-original.pdf

Indicative learning resources - Web based and electronic resources

• ELE page:

Key words search

probability, statistical distribution, regression, survival analysis, Cox model, mixed effects model, bias, confounding, causation, Bayesian methods, research design