Applied Longitudinal Data Analysis
Doctoral course within the doctoral programme in Epidemiology
Course number: 2798
Credit point: 2,5
At the completion of the course, students will be able to:
1) Describe the statistical methods utilized to analyze longitudinal data in a variety of settings and with a variety of types of outcome variables.
2) Analyze a scientific problem that requires repeated measurements, identify an appropriate design, and identify the statistical methods required to analyze the data.
3) Utilize statistical software (e.g., Stata) to perform longitudinal analyses of data generated from randomized and observational studies with repeated measures designs.
4) Apply modern methods for the analysis of longitudinal data to a range of settings encountered in biomedical and public health research
5) Interpret and communicate the clinical/scientific meaning of the results of a longitudinal analysis.
The aim of the course is to introduce modern methods for the analysis of longitudinal and repeated measures studies which are commonly used in epidemiological studies and in clinical trials. Topics include an introduction to the analysis of longitudinal data, the analysis of response profiles, fitting parametric curves, covariance pattern models, random effects and growth curve models, generalized linear models for longitudinal data including generalized estimating equations (GEE), and generalized linear mixed models (GLMMs). The course is intended for all students interested in epidemiology, biostatistics and public health.
Lectures, computer lab with exercises focusing on analysis of real data sets using statistical software (Stata), group discussions, literature review.
Nicholas P. Jewell
|Address:||University of California, Berkeley, School of Public Health|