Topics include generalized linear models; methods for handling incomplete data and censored data; survival analysis and methods for analysis of multilevel (including longitudinal) data. Applications to real world data.
|Paper title||Regression Models for Complex Data|
|Teaching period||Semester 2 (On campus)|
|Domestic Tuition Fees (NZD)||$1,206.91|
|International Tuition Fees||Tuition Fees for international students are elsewhere on this website.|
- STAT 401 or (STAT 270 and STAT 310)
- Teaching staff
- Diggle, P., Heagerty, P., Liang K.Y., Zeger, S.(2002) Analysis of Longitudinal Data Oxford University Press, Oxford.
- McCullagh and Nelder (1989) Generalized linear models, Chapman and Hall.
- Fiztmaurice, Laird and Ware. Applied longitudinal analysis 2nd edition.
- Kalbfleish and Prentice. The statistical analysis of failure time data. 2nd edition.
- Collett. Modelling survival data in medical research (3rd edition).
- Dirk Moore, Applied Survival analysis using R.
- Dobson and Barnett (2008) An Introduction to generalized linear models, Chapman and Hall (3rd edition).
- Graduate Attributes Emphasised
Communication, Critical Thinking, Interdisciplinary perspective, Lifelong learning, Information Literacy, Research, Self motivation, Scholarship, Teamwork
View more information about Otago's graduate attributes.
- Learning Outcomes
Students who successfully complete the paper will be able to:
- Develop an appropriate statistical model for a research question, selecting from a range of regression models (generalised linear models, models for time to event data and multilevel models)
- Describe the characteristics of each type of regression model, including parameter estimation and interpretation, inference and model assumptions
- Carry out a statistical analysis using an appropriate regression model
- Provide clear and succinct written and oral reports on statistical methods and the results of analyses