An analysis of 24-h ambulatory blood pressure monitoring data using orthonormal polynomials in the linear mixed model

The use of 24-h 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 using analytical and graphical results, developing alternative data analysis approaches using modern statistical techniques 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 (mean) and subject-specific trajectories for ABPM data. We propose using a linear mixed model with orthonormal polynomials across time in both the fixed and random effects to analyze ABPM data.

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.

The linear mixed model is relatively easy to implement (given the complexity of the technique) using available software, allows for straightforward testing of multiple hypotheses, and the results can be presented to research clinicians using both graphical and tabular displays. Using orthonormal polynomials provides the ability to model the nonlinear trajectories of each subject with the same complexity as the mean model (fixed effects).