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.
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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 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 Research Institute |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1428689 |
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