Further Regression Analysis (online) - HEA00094M
- Department: Health Sciences
- Credit value: 10 credits
- Credit level: M
- Academic year of delivery: 2022-23
Related modules
Module will run
Occurrence | Teaching period |
---|---|
C | Summer Term 2022-23 |
Module aims
To equip students with the necessary skills and knowledge to allow analysis of data with the ability to handle effect modification, confounding and model diagnostics. By means of lectures and hands-on analysis of data from real health-related studies, using the statistical software package STATA the student is guided through a range of generalised linear models, semi-parametric models such as Cox regression, and bootstrapping. Special attention is paid to the conditions under which the technique may or may not be applied
Module learning outcomes
At the end of the module, students will be able to:
- Make effective use of the statistical package STATA for analysis and interpret the results.
- Correctly construct multivariate generalised linear models, semi-parametric and non-parametric models with emphasis on handling effect modification and carrying regression diagnostics
Module content
Module content
Introduction to STATA
Further multiple regression
Further multiple regression: Introducing interaction terms, more on diagnostics tools for multiple regression including transformations and collinearity
Multiple Logistic regression
Multiple Logistic regression including interaction terms, goodness-of- fit for multiple logistic regression and discrimination
Survival analysis
Principles of survival analysis and introduction of Cox’s regression for time related data
Poisson regression
Poisson regression for count data
Further non-parametric tests and bootstrapping
Indicative assessment
Task | % of module mark |
---|---|
Open Exam (1 day) | 100 |
Special assessment rules
None
Indicative reassessment
Task | % of module mark |
---|---|
Open Exam (1 day) | 100 |
Module feedback
Cohort feedback will be provided in line with Departmental policy for examinations. The exam is computer-based.
Indicative reading
Fox, John . Applied regression analysis and generalized linear models. Sage
Hamilton, L. Statistics with Stata, Wadsworth.
Harrell, Frank E . Regression modeling strategies. Springer-Verlag New York Inc
Hosmer, David W. et al Applied logistic regression. Wiley
Hosmer, David W et al. Applied survival analysis. Wiley-Interscience
Rabe-Hesketh, S. and Everitt, B. A handbook of statistical analyses using Stata, Chapman & Hall.