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Modelling life course blood pressure trajectories using Bayesian adaptive splines

Muniz-Terrera, G; Bakra, E; Hardy, R; Matthews, FE; Lunn, D; FALCon collaboration group, .; (2016) Modelling life course blood pressure trajectories using Bayesian adaptive splines. Statistical Methods in Medical Research , 25 (6) pp. 2767-2780. 10.1177/0962280214532576. Green open access

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Abstract

No single study has collected data over individuals' entire lifespans. To understand changes over the entire life course, it is necessary to combine data from various studies that cover the whole life course. Such combination may be methodologically challenging due to potential differences in study protocols, information available and instruments used to measure the outcome of interest. Motivated by our interest in modelling blood pressure changes over the life course, we propose the use of Bayesian adaptive splines within a hierarchical setting to combine data from several UK-based longitudinal studies where blood pressure measures were taken in different stages of life. Our method allowed us to obtain a realistic estimate of the mean life course trajectory, quantify the variability both within and between studies, and examine overall and study specific effects of relevant risk factors on life course blood pressure changes.

Type: Article
Title: Modelling life course blood pressure trajectories using Bayesian adaptive splines
Open access status: An open access version is available from UCL Discovery
DOI: 10.1177/0962280214532576
Publisher version: http://dx.doi.org/10.1177/0962280214532576
Language: English
Additional information: This article is distributed under the terms of the Creative Commons Attribution 3.0 License (http://www.creativecommons.org/licenses/by/3.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (http://www.uk.sagepub.com/aboutus/openaccess.htm).
Keywords: Adaptive Bayesian splines, blood pressure, hierarchical models, repeated measurements, reversible jump Markov chain Monte Carlo, spline regression
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Education
UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education
UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education > IOE - Social Science
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Pop Health Sciences > Institute of Cardiovascular Science
URI: https://discovery.ucl.ac.uk/id/eprint/1428689
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