Course Description
Studies in which data are collected
repeatedly on a sample of individuals over time are common in the health,
social, and behavioral sciences; agricultural and biological sciences;
education; economics; and business. Questions of interest in the context of
such longitudinal data often focus on patterns of change of outcomes of
interest over time and on identifying factors that are associated with patterns
of change in relevant populations of individuals.This course covers statistical models for drawing scientific inferences
from longitudinal data. Topics include: longitudinal study design, exploring
longitudinal data, linear and general linear regression models for correlated
data, including marginal, random effects and transition models, and handling
missing data.
Course
Objective
To provide theoretical
concepts on fundamental statistical models and methods for the
analysis of longitudinal data, and their implementation using SAS and R by
considering real datasets.
Prerequisite
•
Basic understanding on Generalized linear
Models
•
Familiarity with SAS
-
How to read data from a file
-
How perform simple data manipulations
-
Basic use of simple procedures such as
PROC GLM.
•
Familiarity with R
-
Data management in R
-
Installing packages (i.e. nlme,lme4,)
-
Fitting standard GLM models
NOTE: Lecture slides,
projects, datasets and codes, and reference books and/or reading material are
available in the course webpage.
