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Advanced Regression Analysis - HEA00147M

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• Department: Health Sciences
• Credit value: 20 credits
• Credit level: M
• Academic year of delivery: 2023-24
  • See module specification for other years: 2024-25

Module summary

This module will enable you to advance your understanding and skills in using statistical analysis techniques that are widely used in quantiative health research for outcomes such as time-to-event data, count data, and multilevel data. You will be able to analyse these data using techniques such as survival analysis and count regression. In the case of complex data, where your observations are nested within bigger units, such as hospitals and schools, you will learn how to use multilevel regression to account for this nesting by means of lectures and hands-on analysis of data from real health related studies. You will further your skills in using the statistical software package STATA to perform these analyses. You will also have the opportunity to interpret statistical models in the presence of effect modification and evaluate the use of similar statistical analyses in published research.

Module will run

Occurrence	Teaching period
A	Semester 2 2023-24

Module aims

To equip students with the necessary skills and knowledge to allow analysis of: time-to-event data, count data, and multilevel data; by means of lectures and hands-on analysis of data from real health related studies, using the statistical software package STATA. Special attention is paid to interpretation of models in the presence of effect modification and model diagnostics.

To be able to define commonly used terms in survival analysis and multilevel data analysis.

To evaluate the use of similar statistical analyses in published research.

Module learning outcomes

By the end of the module, students will be able to:

1. Articulate the principles underlying the analysis of time-to-event data, count data, and multilevel data.

2. Carry out survival analysis, count regression and multilevel linear regression.

3. Apply the principles underlying effect modification in regression analysis.

4. Be able to carry out and interpret effect modification in regression analysis.

5. Critically appraise reports and results of research which have used the aforementioned methods.

6. Interpret the results of research which have used the aforementioned regression techniques.

7. Be able to use STATA for analysing data.

Module content

Further multiple linear and logistic regression

• Introducing interaction terms in multiple linear and logistic regression. More on diagnostics tools for multiple multiple linear and logistic regression

Survival analysis

• Introduce principles of survival analysis for time-to-event data and Cox’s regression

Count data regression

• Introduce principles of Poisson and Negative binomial regression for count data

Multil-level Data Analysis

• Introduce a range of multilevel structures, e.g nesting and cross classification, with examples from real studies, introduce how to represent multilevel structures using subscripts, distinguish between levels and variables, and fixed and random effects.

• Introduce the random intercept and the random slope models pointing out why standard linear regression does not work in the case of multilevel structures together with the assumptions underlying these models

Indicative assessment

Task	% of module mark
Online Exam -less than 24hrs (Centrally scheduled)	100

Special assessment rules

None

Indicative reassessment

Task	% of module mark
Online Exam -less than 24hrs (Centrally scheduled)	100

Module feedback

Written cohort feedback for the summative assessment is provided on the standard proforma, 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.

Gelman, A. and Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models: Analytical Methods for Social Research. New York, Cambridge University Press.

Goldstein, H. (2010). Multilevel Statistical Models. 4th edn. Singapore: Wiley Series in Probability and Statistics.

Rabe-Hesketh, S. and Skrondal, A. (2008). Multilevel and Longitudinal Modeling Using Stata. 2nd edn. USA: Stata Press.