A review of methods for modelling both Gaussian and Non-Gaussian longitudinal data with application.
Loading...
Date
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
The study of longitudinal data plays an integral role in medicine, epidemiology, social science,
biomedical and health sciences research where repeated measurements are obtained
over time for each individual. Generally, the interest is in the dependence of the outcome
variable on the covariates. The analysis of the data from longitudinal studies requires special
techniques, which take into account the fact that the repeated measurements within one
individual are correlated. In review of this work, we explore modern developments in the
area of linear and nonlinear generalized mixed-effects regression models and various alternatives
including generalized estimating equations for analysis of longitudinal data and correspondence
analysis (CA). Methods are described for continuous and normally distributed
as well as categorical variables. We apply this theory to the analysis of complete longitudinal
data from National Institute of Environment Health Sciences (NIEHS) focusing on the
relationship between blood lead levels (PbB) and some associated covariates. The results
show that Placebo-treated children had a gradual (occuring) decrease in blood lead level.
Succimer-treated children had an abrupt (unexpected) drop in blood lead level, followed by
rebound. The average mean blood lead level of the succimer-treated children after initiation
of treatment was 19.14 μg/dL lower than that of placebo-treated children. After randomization,
blood lead levels had fallen by similar amounts in both chelated and placebo children,
despite the immediate drops in the chelated group; there was no association between change
in blood lead level and change in cognitive test score. Blood lead levels continued to fall.
Description
M. Sc. University of KwaZulu-Natal, Pietermaritzburg 2015.
Keywords
Gaussian distribution., Longitudinal method., Linear models (Statistics), Theses -- Statistics.