| Unit name | Advanced Statistics |
|---|---|
| Unit code | BRMSM0058 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Dr. Tom Palmer |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
BRMSM0055 - Introduction to Statistical and Epidemiological Methods BRMSM0056 - Regression Models |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | Bristol Medical School |
| Faculty | Faculty of Health Sciences |
The aims of this unit are to:
1) Explain the analysis options available for survival analysis with non-proportional hazards.
2) Demonstrate the link between Poisson regression and Cox regression.
3) Introduce parametric and flexible parametric survival models (such as modelling survival data using parametric distributions such as the exponential, Weibull, and log-logistic distributions, and modelling the baseline hazard using restricted cubic splines).
4) Explain how to analyse survival data with competing outcomes.
5) Introduce the concept of hierarchical data (i.e., longitudinal, and clustered data) and the consequences of analysing the data ignoring its hierarchical structure.
6) Describe the two main approaches for analysing hierarchical data: generalised linear mixed effects models and generalised estimating equations models
7) Discuss the implications of missing data and methods to account for missing data.
8) Introduce available methods to eliminate or reduce the potential bias due to information bias and selection bias.
9) Demonstrate how to design and carry out a reproducible simulation study to investigate the statistical properties of an estimator; including assessing its bias, coverage, and statistical power.
10) Introduce the use of propensity scores as a method to adjust for confounding, and Marginal Structural Models to adjust for time dependent confounding in survival models.
Concepts and methodology will be illustrated using examples from published research.
On successful completion of the unit, a student should be able to:
1. Use statistical software to manipulate, describe and analyse data
2. Conduct analyses using appropriate regression models, considering study design, type of outcome variable, and potential confounders
3. Identify potential sources of bias and apply appropriate statistical methods to account for these sources of bias.
4. Interpret the results from regression models of epidemiological data, considering study design issues.
5. Write a clear report describing, presenting, and interpreting results from analyses.
• There will be 10 teaching weeks.
• Teaching will include learning activities including lectures, small group work, discussions, individual tasks, and practicals.
• Directed and self-directed learning will include activities such as reading, accessing web-based supplementary materials, critical analysis, and completion of assessments.
• 150 hours of directed and self-directed learning. The directed learning includes 50 hours of teaching and the self-directed learning includes activities such as reading, quizzes, and multi-media learning.
There will be two types of formative assessments. The first type will support student learning by using informal questioning, quizzes and group exercises in lectures and tutorials. These assessments are for learning and will not contribute to the final unit mark (ILOs 1-4).
The second formative assessment will be a data analysis and interpretation exercise of a dataset from a clustered study design or a longitudinal study design. Students will be given a dataset and a research question, and asked to formulate an analysis plan, carry out the analysis and write a report. The report will include an introduction to the research question, a description of the analysis carried out, appropriate presentation of relevant results, a discussion and references. Students will be provided with a model answer to the exercise and will be asked to carry out peer-marking in groups. (ILOs 1,2,4,5)
The summative assessment will be in the form of a data analysis and interpretation exercise from any study design covered in teaching block 1 or in this unit. Students will be given a dataset and a research question, and asked to formulate an analysis plan, carry out the analysis and write a 2,500-word report (excluding references, tables and figures). The report will include an introduction to the research question, a description of the analysis carried out, appropriate presentation of relevant results, a discussion, and references (ILOs 1-5).
An overall score of 50% will be required to pass the unit.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. BRMSM0058).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
