Course catalogue doctoral education - VT20

    Startpage
  • Ansökan kan ske mellan 2019-10-16 och 2019-11-15
Login or register to be able to apply this course

Print
Title Applied longitudinal data analysis
Course number 2798
Programme Epidemiologi
Language English
Credits 2.5
Date 2020-04-28 -- 2020-05-07
Responsible KI department Institutionen för medicinsk epidemiologi och biostatistik
Specific entry requirements Knowledge in epidemiology and biostatistics equivalent to ""Epidemiology I: Introduction to epidemiology"", ""Biostatistics I: Introduction for epidemiologists"" and ""Biostatistics II: Logistic regression for epidemiologists"" or corresponding courses.
Purpose of the course The course gives an introduction to modern methods for the analysis of longitudinal and repeated measures studies which are commonly used in epidemiological studies and in clinical trials.
Intended learning outcomes After successfully completing this course you as a student are expected to be able to:

- Describe the statistical methods utilized to analyze longitudinal data in a variety of settings and with a variety of types of outcome variables.

- Analyze a scientific problem that requires repeated measurements, identify an appropriate design, and identify the statistical methods required to analyze the data.

- Utilize statistical software (e.g., Stata) to perform longitudinal analyses of data generated from randomized and observational studies with repeated measures designs.

- Apply modern methods for the analysis of longitudinal data to a range of settings encountered in biomedical and public health research.

- Interpret and communicate the clinical/scientific meaning of the results of a longitudinal analysis.

Intended learning outcomes are classified according to Bloom¿s taxonomy: knowledge, comprehension, application, analysis, synthesis, and evaluation (Bloom, 1956, extended by Anderson and Krathwohl, 2001).
Contents of the course The course gives an introduction to modern methods for the analysis of longitudinal and repeated measures studies which are commonly used in epidemiological studies and in clinical trials. The defining feature of a longitudinal study is that measurements of the response are taken repeatedly through time on the same individuals. The primary goal of a longitudinal study is to characterize an outcome (and potentially change in that outcome over time) and the factors that influence the outcome (and its change). A feature of longitudinal data that complicates analysis is the positive correlation (i.e., lack of independence) among repeated observations and possible heterogeneity of variability across measurement occasions. The course covers the following topics: Introduction to longitudinal data, notation for correlated data, modeling the mean response (analysis of response profiles, parametric and semi-parametric trends), modeling the covariance, growth curves (trajectories), fixed effects models, and mixed effects models (that include random effects). This course is focused on general regression models for longitudinal data when the response variable is either continuous (linear models) or discrete (e.g., binary or count data that require logistic and Poisson models). Topics covered in the course will include: introduction to generalized linear models (e.g., linear, logistic, and Poisson regression), extensions of generalized linear models to longitudinal data, marginal models and generalized estimating equations (GEE), random effects models for continuous and categorical data (generalized linear mixed models), and contrasting marginal and mixed effects models. The course is intended for all students interested in epidemiology, biostatistics and public health.
Teaching and learning activities Lectures, computer lab with exercises focusing on analysis of real data sets using statistical software (Stata), group discussions, literature review.
Compulsory elements The individual written examination (summative assessment).
Examination To pass the course, the student has to show that the learning outcomes have been achieved. Assessments methods used are group assignments (formative assessments) and an individual written take-home examination (summative assessment). The focus will be on understanding concepts and their application to analysis of epidemiological studies, rather than mathematical detail. The examination is viewed as a contributing to the development of knowledge, rather than as a test of knowledge. Students who do not obtain a passing grade in the first examination will be offered a second examination within two months of the final day of the course. Students who do not obtain a passing grade at the first two examinations will be given top priority for admission the next time the course is offered. If the course is not offered during the following two academic terms, a third examination will be scheduled within 12 months of the final day of the course.
Literature and other teaching material Recommended reading:

Fitzmaurice GM, Laird NM, Ware JH. Applied Longitudinal Analysis, 2nd Ed. Wiley & Sons, 2011

PDF course notes of some chapters of prospective book Jewell NP, Hubbard A, Heggeseth, B, Analysis of Longitudinal Studies in Epidemiology (to be distributed)

Number of students 8 - 25
Selection of students Eligible doctoral students will be prioritized according to 1) the relevance of the course syllabus for the applicant’s doctoral project (according to written information), 2) date for registration as a doctoral student (priority given to earlier registration date). To be considered, submit a completed application form. Give all information requested, including a short description of current research training and motivation for attending, as well as an account of previous courses taken.
More information Course dates are April 28, 29, 30 and May 4, 5, 6, 7. Course leader is Nicholas P. Jewell, Professor of Biostatistics and Epidemiology at the London School of Hygiene & Tropical Medicine, London, UK and at the University of California, Berkeley, USA.
Additional course leader
Latest course evaluation Course evaluation report
Course responsible Rino Bellocco
Institutionen för medicinsk epidemiologi och biostatistik

Rino.Bellocco@ki.se

.



Contact person Gunilla Nilsson Roos
Institutionen för medicinsk epidemiologi och biostatistik
08-524 822 93
gunilla.nilsson.roos@ki.se