Selecting and fitting graphical chain models to longitudinal data
Selecting and fitting graphical chain models to longitudinal data
The aim of this paper is to demonstrate how graphical chain models can be used as effective tools in life course research focusing in particular on models for longitudinal prospective data. The substantive research question focuses on whether young motherhood is a pathway through which socio-economic disadvantage in childhood is related to poor self-reported health in adulthood among the 1970 British birth cohort. By breaking down large multivariate systems into simpler more tractable subcomponents and analysing them via local regressions, graphical models helps the understanding of complicated life course processes, show the intermediate relationships between predictors, and aid the understanding of the mechanisms through which potential confounding and mediating factors affect the outcome of interest.
Southampton Statistical Sciences Research Institute, University of Southampton
Borgoni, Riccardo
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Berrington, Ann M.
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Smith, Peter W. F.
961a01a3-bf4c-43ca-9599-5be4fd5d3940
2004
Borgoni, Riccardo
df9c90ab-c2d2-47d6-bcc7-1444a605d6ff
Berrington, Ann M.
bd0fc093-310d-4236-8126-ca0c7eb9ddde
Smith, Peter W. F.
961a01a3-bf4c-43ca-9599-5be4fd5d3940
Borgoni, Riccardo, Berrington, Ann M. and Smith, Peter W. F.
(2004)
Selecting and fitting graphical chain models to longitudinal data
(S3RI Methodology Working Papers, M04/05)
Southampton, UK.
Southampton Statistical Sciences Research Institute, University of Southampton
33pp.
Record type:
Monograph
(Project Report)
Abstract
The aim of this paper is to demonstrate how graphical chain models can be used as effective tools in life course research focusing in particular on models for longitudinal prospective data. The substantive research question focuses on whether young motherhood is a pathway through which socio-economic disadvantage in childhood is related to poor self-reported health in adulthood among the 1970 British birth cohort. By breaking down large multivariate systems into simpler more tractable subcomponents and analysing them via local regressions, graphical models helps the understanding of complicated life course processes, show the intermediate relationships between predictors, and aid the understanding of the mechanisms through which potential confounding and mediating factors affect the outcome of interest.
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Published date: 2004
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Local EPrints ID: 8178
URI: http://eprints.soton.ac.uk/id/eprint/8178
PURE UUID: 8c06ee77-05aa-4c50-b555-1aa0f685fef8
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Date deposited: 11 Jul 2004
Last modified: 16 Mar 2024 02:46
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Contributors
Author:
Riccardo Borgoni
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