Statistics for Health and Life Sciences
| Module title | Statistics for Health and Life Sciences |
|---|---|
| Module code | HPDM182 |
| Academic year | 2024/5 |
| Credits | 15 |
| Module staff | Dr Eilis Hannon (Convenor) Dr Harry Green (Convenor) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 10 | 0 | 0 |
| Number students taking module (anticipated) | 40 |
|---|
Module description
This module provides a broad introduction to statistical analysis for health and life science applications. 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. Key concepts in probability theory and the role of statistical distributions in modelling health data will be covered. The core part of the module provides a foundation in regression modelling to include simple linear regression, logistic regression, survival analysis and models that account for complex temporal and hierarchical data structures. Embedded through the module is a strong emphasis on the critical evaluation of statistical methodology and interpretation of analysis results in the context of the specific health application. Throughout this module, you will gain practical experience of statistical computing using the R software environment and exposure to case studies based on real-world health data.
Module aims - intentions of the module
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. Examine and apply fundamental concepts in hypothesis testing including sampling, probability and statistical distributions.
- 2. Apply a range of statistical inference methods to address health data science problems including both simple and advanced regression models.
- 3. Interpret and critically evaluate the results of statistical inference in the context of a specified quantitative health research question.
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 4. Formulate health research questions as statistical problems
- 5. 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...
- 6. Use the R software environment for statistical computing
- 7. Understand and critically appraise academic research papers in research field
- 8. Effectively communicate arguments, evidence and conclusions using a variety of formats in a manner appropriate to the intended audience
Syllabus plan
Whilst the module’s precise content may vary from year to year, an example of an overall structure is as follows:
- 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)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 35 | 115 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled Learning and Teaching | 15 | Lectures (10 x 1.5 hours) |
| Scheduled Learning and Teaching | 20 | Computer based workshops (10 x 2 hours) |
| Guided independent study | 85 | Background reading and preparation for written assessment |
| Guided independent study | 30 | Preparation for group assessment |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Multiple choice questions will be given as part of the workshops, and will be self-assessed | 5 questions for each workshop session | 1-5 | Oral staff where required |
| Example questions with model solutions | 1 worksheet per workshop | 1-6 | Self-assessed |
| Peer review | 300 word written research proposal | 1, 4, 7, 8 | Written |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 0 | 0 | 0 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Group project (in groups of 3/4) | 40 | 10 minute presentation with Q&A (90%) and portfolio of evidence of collaborative working (10%) | 1-8 | Written |
| Written assignment | 60 | maximum 2000 words | 1-8 | Written |
Details of re-assessment (where required by referral or deferral)
| Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
|---|---|---|---|
| Group presentation (40%) | Individual presentation 10 minutes | 1-8 | Typically within six weeks of the result |
| Written assignment (60%), 15maximum 2000 words | Written assignment | 1-8 | Typically within six weeks of the result |
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
- 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: https://vle.exeter.ac.uk/course/view.php?id=8441
| Credit value | 15 |
|---|---|
| Module ECTS | 7.5 |
| Module pre-requisites | None |
| Module co-requisites | HPDM092 Fundamentals of Research Design |
| NQF level (module) | 7 |
| Available as distance learning? | No |
| Origin date | 26/04/2024 |
