Modelling life course blood pressure trajectories using Bayesian adaptive splines.
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.
MRC Unit for Lifelong Health and Ageing at UCL, London, UK g.muniz@ucl.ac.uk.
Graciela
G
Muniz-Terrera
MRC Biostatistics Unit.
Eleni
E
Bakra
MRC Unit for Lifelong Health and Ageing at UCL, London, UK.
Rebecca
R
Hardy
MRC Biostatistics Unit.
Fiona E
FE
Matthews
MRC Biostatistics Unit.
David
D
Lunn
FALCon Collaboration Group
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001499:Bayes Theorem; D001794:Blood Pressure / Q000502:physiology*; D002648:Child; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D008390:Markov Chains; D008875:Middle Aged; D009010:Monte Carlo Method; D006113:United Kingdom; D055815:Young Adult
adaptive Bayesian splines; blood pressure; hierarchical models; repeated measurements; reversible jump Markov chain Monte Carlo; spline regression