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STAT440 Longitudinal Data Analysis

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
Paper code STAT440
Subject Statistics
EFTS 0.1667
Points 20 points
Teaching period Second Semester
Domestic Tuition Fees (NZD) $1,076.55
International Tuition Fees (NZD) $4,267.52

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Prerequisite
STAT 341, STAT 362
Eligibility
Students who have completed 300-level papers in statistics - STAT 341 and STAT 362 in particular.
Contact
Dr Matthew Parry
Teaching staff
Dr Jiaxu Zeng
Paper Structure
Topics:
  • 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).
Textbooks
Required text:
  • Fitzmaurice, G.M., Laird, N.M., and Ware J.H. (2011) Applied Longitudinal Analysis Wiley
Useful references:
  • 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.

^ Top of page

Timetable

Second Semester

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
Other

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
Paper code STAT440
Subject Statistics
EFTS 0.1667
Points 20 points
Teaching period Second Semester
Domestic Tuition Fees Tuition Fees for 2018 have not yet been set
International Tuition Fees Tuition Fees for international students are elsewhere on this website.

^ Top of page

Prerequisite
STAT 341, STAT 362
Eligibility
Students who have completed 300-level papers in statistics - STAT 341 and STAT 362 in particular.
Contact
Dr Matthew Parry
Teaching staff
To be confirmed.
Paper Structure
Topics:
  • 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).
Textbooks
Required text:
  • Fitzmaurice, G.M., Laird, N.M., and Ware J.H. (2011) Applied Longitudinal Analysis Wiley
Useful references:
  • 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.

^ Top of page

Timetable

Second Semester

Location
Dunedin
Teaching method
This paper is taught On Campus
Learning management system
Other