Journal article
Bayesian hierarchical piecewise regression models: a tool to detect trajectory divergence between groups in long-term observational studies
MJ Buscot, SS Wotherspoon, CG Magnussen, M Juonala, MA Sabin, DP Burgner, T Lehtimäki, JSA Viikari, N Hutri-Kähönen, OT Raitakari, RJ Thomson
BMC Medical Research Methodology | BMC | Published : 2017
Abstract
Background: Bayesian hierarchical piecewise regression (BHPR) modeling has not been previously formulated to detect and characterise the mechanism of trajectory divergence between groups of participants that have longitudinal responses with distinct developmental phases. These models are useful when participants in a prospective cohort study are grouped according to a distal dichotomous health outcome. Indeed, a refined understanding of how deleterious risk factor profiles develop across the life-course may help inform early-life interventions. Previous techniques to determine between-group differences in risk factors at each age may result in biased estimate of the age at divergence. Method..
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Awarded by Turun Yliopisto
Funding Acknowledgements
The Young Finn study was financially supported by the Academy of Finland (grants 126925, 121584, 124282, 129378, 117797, 265877 and 41071), the Social Insurance Institution of Finland, the Turku University Foundation, Paavo Nurmi Foundation, Juho Vainio Foundation, Sigrid Juselius Foundation, Maud Kuistila Foundation, Research funds from the Kuopio, Turku and Tampere University Hospitals, the Finnish Foundation of Cardiovascular Research, the Finnish Medical Foundation, the Orion-Farmos Research Foundation, and the Finnish Cultural Foundation. This work was partly funded by the National Health and Medical Research Council Project Grant (APP1098369). CGM was supported by a National Heart Foundation of Australia Future Leader Fellowship (100849).