SFOK18003U Statistical analysis of repeated measurements with mixed models
MSc in Public Health Science - elective course
This course is also open for students in the following programmes, but please be aware of the recommended academic qualifications:
MSc in Global Health
MSc in Health Informatics
MSc in Human Biology
MSc in Health Science
This course is concerned with analysis of correlated
quantitative data arising
e.g. when taking obsertions repeatedly over time on the same
subjects or from clusters of subjects such as siblings, families,
school classes or patients belonging to the same cilic. Pitfalls of
traditional statistical analyses will be
discussed and appropriate models for the analysis of e.g. baseline
follow-up studies, cross-over trials, and cluster randomized
trials will be exemplified.
Day 1: Basic concepts for repeated measurements.
Descriptive
statistics. Baseline follow-up studies. Linear mixed models.
Handling repeated measurements in SAS or R.
Day 2: Longitudinal data analysis. Modeling mean and covariance. Balanced and unbalanced data.
Day 3: Analysis of clustered data. Variance components. Multi-level models. The linear growth model.
Day 4: Select applications of linear mixed
models. Cross-over trials.
Repeatability and reproducibility of measurement methods
Day 5: Models for binary and other non-normally distributed data. Generalized linear mixed models. Marginal models and generalized estimating equations.
Day 6: Missing data. Consequences and statistical methods for handling. Course summary and evaluation.
After completing the course, the students are expected to be able to:
Knowledge
Have a working knowledge of statistical methods for analysing data from repeated measurements and clustered data designs including baseline-follow-up studies, cross over trials, studies of reproducibility, cluster randomized tirals, and multilevel models.
Have a working knowledge about linear mixed models for quantitative outcomes as well as generalized linear models and generalized estimating equation for non-normally distributed outcomes.
Reflect on the statistical consequences of using repeated measurements or clustered designs compared to independent samples designs.
Reflect on potential biases due to missing data in studies with repeated measurements or clustered data.
Skills
Perform analyses of repeated measurement and clustered data including baseline-follow-up studies, cross over trials, studies of reproducibility, cluster randomized tirals, and multilevel models using SAS or other statistical software.
Interpret the results of the above-menationed analyses of repeated measurements or clustered data.
Assess the validity and potential limitations of the results from the above-mentioned analyses of repeated measurements or clustered data.
Present results from analyses of repeated measurements or clustered data in figures, numbers, and text.
Read and discuss statistical results from analysis of repeated measurements or clustered data presented in the health science literature.
Competences
Independently plan and conduct analyses of repeated measurements or clustered data as part of your own master, ph.d., or other health science project.
Collaborate with a statistician in planning and conducting analyses of repeated measurements or clustered dat as part of your own master, ph.d., or other health science project.
Take part in dicussing results of health science research that is based on analysis of repeated measurements or clustered data.
Text book: G.M. Fitzmaurice, N.M. Laird and J.H. Ware, Applied Longitudinal Analysis (2nd edition), John Wiley & sons, 2011.
Please note that an e-book version can be downloaded from the Royal Library.
Lecture notes, exercise problems, datasets and other supplementary material will be made available at the course webpage
http://publicifsv.sund.ku.dk/~jufo/RepeatedMeasures2018.html
as the course proceeds.
Finally students must be familiar with either SAS or R programming to be able to solve the exercise problems.
- Category
- Hours
- Class Instruction
- 18
- Exam
- 56
- Lectures
- 18
- Preparation
- 46
- Total
- 138
All students may receive personal supervision from the teachers during the exercise classes. You are encouraged to engage in discussion with the teachers and your fellow students in particular during exercise classes, but questions during lectures are also welcome.
Students planning to analyse repeated measurements data in their master or ph.d. project are asked to reflect on their research project in class.
Open for credit transfer students and other external students. Apply here:
Credit transfer students:
http://healthsciences.ku.dk/education/other-programme-options/credit-transfer-students/
Other external students:
http://healthsciences.ku.dk/education/exchange_guest_students/guest-students/
- Credit
- 5 ECTS
- Type of assessment
- Written assignment, 1 weekWritten take home exam. Students may discuss the exam problems but are requested to hand in individual reports.
- Aid
- All aids allowed
- Marking scale
- passed/not passed
- Censorship form
- No external censorship
Ingen censur.
- Exam period
- Re-exam
Please see the exam schedule at KUnet
The exam form in the re-examination may differ from the ordinary exam. Should this happen, students registered for the re-examination will be informed as soon as possible.
Criteria for exam assesment
To pass the exam, the students are expected to be able to:
Knowledge
Have a working knowledge of statistical methods for analysing data from repeated measurements and clustered data designs including baseline-follow-up studies, cross over trials, studies of reproducibility, cluster randomized tirals, and multilevel models.
Have a working knowledge about linear mixed models for quantitative outcomes as well as generalized linear models and generalized estimating equation for non-normally distributed outcomes.
Reflect on the statistical consequences of using repeated measurements or clustered designs compared to independent samples designs.
Reflect on potential biases due to missing data in studies with repeated measurements or clustered data.
Skills
Perform analyses of repeated measurement and clustered data including baseline-follow-up studies, cross over trials, studies of reproducibility, cluster randomized tirals, and multilevel models using SAS or other statistical software.
Interpret the results of the above-menationed analyses of repeated measurements or clustered data.
Assess the validity and potential limitations of the results from the above-mentioned analyses of repeated measurements or clustered data.
Present results from analyses of repeated measurements or clustered data in figures, numbers, and text.
Competences
Course information
- Language
- English
- Course code
- SFOK18003U
- Credit
- 5 ECTS
- Level
- Full Degree MasterPh.D.
- Duration
- 6 full days of teaching and a subsequent 1 week take-home exam.
- Placement
- Autumn
- Schedule
- Please see the schedule in syllabus.
- Course capacity
- 25 students
- Continuing and further education
- Study board
- The Study Board for Public Health Science and Global Health
Contracting department
- Department of Public Health
Contracting faculty
- Faculty of Health and Medical Sciences
Course Coordinators
- Julie Lyng Forman (4-6f7a6b7445787a736933707a336970)
Section of Biostatistics
Lecturers
Associate professor Julie Lyng Forman, Section of Biostatistics, and others.