Analyzing longitudinal data in the presence of informative drop-out: The jmre1 command
Nikos Pantazis
Department of Hygiene, Epidemiology, and Medical Statistics
University of Athens Medical School
Athens, Greece
[email protected]
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Giota Touloumi
Department of Hygiene, Epidemiology, and Medical Statistics
University of Athens Medical School
Athens, Greece
[email protected]
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Abstract. Many studies in various research areas have designs that involve repeated
measurements over time of a continuous variable across a group of subjects.
A frequent and serious problem in such studies is the occurrence of missing data.
In many cases, missing data are caused by an event that leads to a premature
termination of the series of repeated measurements on some subjects. When the
probability of the occurrence of this event is related to the subject-specific underlying
trend of the variable of interest, this missingness process is called informative
censoring or informative drop-out. Standard likelihood-based methods (for example,
linear mixed models) fail to give consistent estimates. In such cases, one needs
to apply methods that simultaneously model the observed data and the missingness
process. In this article, we review a method proposed by Touloumi et al.
(1999, Statistics in Medicine 18: 1215–1233) to adjust for informative drop-out
in longitudinal data analysis. We also present the jmre1 command, which can be
used to fit the proposed model. The estimation method combines the restricted iterative
generalized least-squares method with a nested expectation-maximization
algorithm. The method is implemented mainly using Stata’s matrix programming
language, Mata. Our example is derived from the epidemiology of the HIV
infection.
View all articles by these authors:
Nikos Pantazis, Giota Touloumi
View all articles with these keywords:
jmre1, jmre1_p, datajoint1, missing data, informative censoring, informative drop-out, longitudinal data
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