STAT402 Regression Models for Complex Data

Overview

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.

About this paper

Prerequisite

STAT 401 or (STAT 270 and STAT 310)

Contact

xun.xiao@otago.ac.nz

Teaching staff

Associate Professor Ting Wang

Textbooks

Recommended reading:

• 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

Timetable