The use of 24-hour ambulatory blood pressure monitoring (ABPM) in clinical
practice and observational epidemiological studies has grown considerably in
the past 25 years. ABPM is a very effective technique for assessing biological,
environmental, and drug effects on blood pressure. In order to enhance the
effectiveness of ABPM for clinical and observational research studies via
analytical and graphical results, developing alternative data analysis
approaches are important. The linear mixed model for the analysis of
longitudinal data is particularly well-suited for the estimation of, inference
about, and interpretation of both population and subject-specific trajectories
for ABPM data. Subject-specific trajectories are of great importance in ABPM
studies, especially in clinical research, but little emphasis has been placed
on this dimension of the problem in the statistical analyses of the data. We
propose using a linear mixed model with orthonormal polynomials across time in
both the fixed and random effects to analyze ABPM data. Orthonormal polynomials
in the linear mixed model may be used to develop model-based, subject-specific
24-hour ABPM correlates of cardiovascular disease outcomes. We demonstrate the
proposed analysis technique using data from the Dietary Approaches to Stop
Hypertension (DASH) study, a multicenter, randomized, parallel arm feeding
study that tested the effects of dietary patterns on blood pressure
