Use of mixed effects models for the analysis of longitudinal data, with an emphasis on applications on biostatistics.
This paper should be of interest to students who want to know more about biostatistics
and longitudinal analysis.
Mixed models are a powerful class of models used for the analysis of correlated data. Examples of correlated data include, but are not limited to, clustered data, repeated observations, longitudinal data, multiple dependent variables, spatial data or data from population pharmacokinetic/pharmacodynamic studies. A key feature of mixed models is that, by introducing random effects in addition to fixed effects, they allow you to address multiple source of variation, e.g. in the longitudinal study they allow you to take into account both within- and between- subject variations.
|Paper title||Longitudinal Data Analysis|
|Teaching period||Not offered in 2020|
|Domestic Tuition Fees (NZD)||$1,142.40|
|International Tuition Fees (NZD)||$4,661.93|
- STAT 341, STAT 362
- Students who have completed 300-level papers in statistics - STAT 341 and STAT 362 in particular.
Dr Matthew Parry (email@example.com)
- More information link
- View further information for STAT 440
- Teaching staff
- To be confirmed.
- Paper Structure
- Introduction to longitudinal and clustered data
- Theory of mixed models
- Linear models for longitudinal continuous data
- Covariance structures
- Random coefficients models
- Generalised linear mixed models
- Generalised Estimating equations
- Multilevel analysis
- Missing data issues
- Teaching Arrangements
- There will be weekly lectures (2 hours) and weekly computer labs (2 hours).
- Required text:
- Fitzmaurice, G.M., Laird, N.M., and Ware J.H. (2011) Applied Longitudinal Analysis Wiley
- Brown,H. and Prescott,R. (1999) Applied Mixed Models in Medicine Wiley, Chinchester
- Littell,R., Milliken,G., Stroup,W., and Wolfinger,R. (1996) SAS System for Mixed Models. SAS Institute Inc., Cary, North Carolina
- Verbeke, G. and Molenberghs, G. (1997) Linear Mixed Models in Practice: A SAS-oriented approach. Springer, New York
- Diggle, P., Heagerty, P., Liang K.Y., Zeger, S.(2002) Analysis of Longitudinal Data. Oxford University Press, Oxford
- Course outline
- View course outline for STAT440
- Graduate Attributes Emphasised
- Communication, Critical thinking, Information literacy, Self-motivation.
View more information about Otago's graduate attributes.
- Learning Outcomes
- Students who successfully complete the paper will develop an ability to analyse longitudinal data.