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Online course materials for MATH68052

Generalised Linear Models and Survival Analysis


Unit code: MATH68052
Credit Rating: 15
Unit level: Level 6
Teaching period(s): Semester 2
Offered by School of Mathematics
Available as a free choice unit?: N

Requisites

None

Additional Requirements

Students are not permitted to take more than one of MATH38052 or MATH48052 for credit in the same or different undergraduate year.  Students are not permitted to take MATH48052 and MATH68052 for credit in an undergraduate programme and then a postgraduate programme.

Aims

This course unit consists of two parts, one is Generalised Linear Models (10 credits) and the other is Survival Analysis (5 credits). For the specification of Generalised Linear Models, see MATH38052.

Overview

The Survival Analysis part of the unit aims to familiarise students with the methodology and practical applications of some standard techniques in modelling and analysing survival data.

Learning outcomes

Learning Outcomes

On successful completion of the survival analysis part of the unit students will be able to

  • carry out exploratory non-parametric analysis of survival data;
  • carry out more sophisticated analyses on survival data;
  • explore and analyse survival data using using statistical packages;
  • interpret the results of such analyses.

Future topics requiring this course unit

This course unit is naturally related to another 4th year unit, Longitudinal Data Analysis.

Assessment methods

  • Other - 20%
  • Written exam - 80%

Assessment Further Information

  • Coursework: 20%
  • End of semester examination: Three hours weighting 80%

Syllabus

  1. Introduction: background, review of linear models in matrix notation, model assessment, some pre-required knowledge. [2]
  2. The exponential family of distributions: Definition and examples. Mean and variance, variance function and scale parameter. [2]
  3. Generalized linear models (GLM): linear predictor, link function, canonical link, maximum likelihood estimation, iterative reweighted least squares and Fisher scoring algorithms, significance of parameter estimates, deviance, Pearson and deviance residuals, Pearsonâ€'s chi-square test and the likelihood ratio test, model fitting using R. [7]
  4. Normal linear regression models: least squares, analysis of variance, orthogonality of parameters, factors, interactions between factors. [2]
  5. Binary and Binomial data analysis: distribution and models, logistic regression models, odds ratio, one- and two-way logistic regression analysis. [5]
  6. Poisson count data analysis: Poisson regression models with offset, two-dimensional contingency tables, log-linear models. [4]
  7. Survival data. Censoring. The survivor, hazard, cumulative hazard functions.  Kaplan-Meier estimate of survivor function. [3]
  8. Fitting exponential and Weibull distributions to survival data. Hazard plots and log cumulative hazard plots. [3]
  9. Proportional hazards (ph) and Cox regression: assumptions and interpretation.. Model fitting and diagnostics. Hazard ratios and confidence intervals. [5]

Recommended reading

  • Dobson, A. J., An Introduction to Generalized Linear Models, Chapman & Hall 2002.
  • Krzanowski, W., An Introduction to Statistical Modelling, Edward Arnold 1998.
  • McCullagh, P. and Nelder, J. A., Generalized Linear Models, Chapman & Hall 1990.
  • Collett, D., Modelling Survival Data in Medical Research, 2nd edition, Chapman and Hall 2004.
  • Klein, J. P. and Moeschberger, M. L., Survival Analysis, 2nd edition, Springer 2003.

Feedback methods

Feedback tutorials will provide an opportunity for students' work to be discussed and provide feedback on their understanding.  Coursework or in-class tests (where applicable) also provide an opportunity for students to receive feedback.  Students can also get feedback on their understanding directly from the lecturer, for example during the lecturer's office hour.

Study hours

  • Lectures - 33 hours
  • Tutorials - 11 hours
  • Independent study hours - 106 hours

Teaching staff

Jingsong Yuan - Unit coordinator

Timothy Waite - Unit coordinator

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