Analysis of 24-Hour Ambulatory Blood Pressure Monitoring Data using Orthonormal Polynomials in the Linear Mixed Model

Details Analysis of 24-Hour Ambulatory Blood Pressure Monitoring Data using Orthonormal Polynomials in the Linear Mixed Model Analysis of 24-Hour Ambulatory Blood Pressure Monitoring Data using Orthonormal Polynomials in the Linear Mixed Model

2010-10-06

arxiv.org/abs/1010.1224v1

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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 studies via graphical and analytical results, developing a unified data analysis approach is paramount. 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 profiles for ABPM data. Subject-specific profiles are of great importance in ABPM studies, especially in clinical practice, but little emphasis has been placed on this dimension of the problem. We propose using a linear mixed model with orthonormal polynomials across time in both the fixed and random effects to analyze ABPM data. Our method provides a powerful approach to the analysis of these data. The results can be used as the basis for standardizing analytical approaches and model-based graphical presentations of ABPM data. We demonstrate the proposed 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. We contrast our approach with the restricted cubic spline linear mixed model proposed by Lambert et al (2001) and demonstrate a better model fit, better predictions for individual subject profiles, and improved computational stability.

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