Further Regression Analysis - HEA00002M
- 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 an awareness of effect modification and confounding. By means of lectures and hands-on analysis of data from real healthrelated studies, using the statistical software package STATA the student is guided through the full range of standard statistical parametric and non-parametric techniques, ranging from frequency tables to Cox's regression. Special attention is paid to the conditions under which the technique may or not may be applied.
Module learning outcomes
Students will be able to:
- Make effective use of the statistical package STATA for analysis.
- Interpret the results of using such packages and generate functional reports.
- Utilise descriptive and inferential statistical tests of difference and association.
- Correctly construct multivariate linear, logistic and Poisson regression models and to undertake survival analysis and Cox-regression modelling.
Module content
Introduction to STATA
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 |
|---|---|
| Online Exam - 24 hrs (Centrally scheduled) | 100 |
Special assessment rules
None
Indicative reassessment
| Task | % of module mark |
|---|---|
| Online Exam - 24 hrs (Centrally scheduled) | 100 |
Module feedback
Students are provided with collective exam feedback relating to their cohort, within the timescale specified in the programme handbook.
Indicative reading
- Belsley, David A. Regression diagnostics. Wiley-Interscience
- 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.