Trial reportBlood pressure monitoring2014
An analysis of 24-h ambulatory blood pressure monitoring data using orthonormal polynomials in the linear mixed model.
Trial report in Blood pressure monitoring, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Statistical Brain Network Analysis.Annual review of statistics and its application · 2024Article
- A mixed-modeling framework for whole-brain dynamic network analysis.Network neuroscience (Cambridge, Mass.) · 2022Article
- Anthropometric Measures Correspond with Functional Motor Outcomes in Females with Rett Syndrome.The Journal of pediatrics · 2022Article
- Mixed Modeling Frameworks for Analyzing Whole-Brain Network Data.Methods in molecular biology (Clifton, N.J.) · 2022Article
- A Joint Model for Unbalanced Nested Repeated Measures with Informative Drop-Out Applied to Ambulatory Blood Pressure Monitoring Data.BioMed research international · 2022Article
- Ambulatory Blood Pressure Trajectory and Perceived Stress in Relation to Birth Outcomes in Healthy Pregnant Adolescents.Psychosomatic medicine · 2019Article
- Out-of-Clinic Sympathetic Activity Is Increased in Patients With Masked Uncontrolled Hypertension.Hypertension (Dallas, Tex. : 1979) · 2019Article
- Morning surge in blood pressure using a random-effects multiple-component cosinor model.Statistics in medicine · 2018Article
- Exploring diurnal variation using piecewise linear splines: an example using blood pressure.Emerging themes in epidemiology · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
backgroundThe 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.
objectivesIn 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.
methodsThe 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.
resultsWe 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.
conclusionThe 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).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.