Background: As persons with HIV are living longer, there is a growing need to investigate
factors associated with chronic disease, rate of disease progression and survivorship. Many risk factors
for this high-risk population change over time, such as participation in treatment, alcohol consumption
and drug abuse. Longitudinal datasets are increasingly available, particularly clinical data that contain
multiple observations of health exposures and outcomes over time. Several analytic options are
available for assessment of longitudinal data; however, it can be challenging to choose the appropriate
analytic method for specific combinations of research questions and types of data. The purpose of this review is to help
researchers choose the appropriate methods to analyze longitudinal data, using alcohol consumption as an example of a
time-varying exposure variable. When selecting the optimal analytic method, one must consider aspects of exposure (e.g.
timing, pattern, and amount) and outcome (fixed or time-varying), while also addressing minimizing bias. In this article,
we will describe several analytic approaches for longitudinal data, including developmental trajectory analysis,
generalized estimating equations, and mixed effect models. For each analytic strategy, we describe appropriate situations
to use the method and provide an example that demonstrates the use of the method. Clinical data related to alcohol
consumption and HIV are used to illustrate these methods.
Keywords: Alcohol, generalized estimating equations, generalized linear mixed models, group-based trajectories, HIV,
longitudinal, marginal structural models, time-varying exposure.
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