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Multivariate multilevel spline models for parallel growth processes: application to weight and mean arterial pressure in pregnancy.

Macdonald-Wallis C, Lawlor DA, Palmer T, Tilling K - Stat Med (2012)

Bottom Line: Growth models are commonly used in life course epidemiology to describe growth trajectories and their determinants or to relate particular patterns of change to later health outcomes.We discuss linear spline multilevel models with a multivariate response and show how these can be used to relate rates of change in a particular time period in one variable to later rates of change in another variable by using the variances and covariances of individual-level random effects for each of the splines.This method improves on the multivariate linear growth models, which have been used previously to model parallel processes because it enables nonlinear patterns of change to be modelled and the temporal sequence of multivariate changes to be determined, with adjustment for change in earlier time periods.

View Article: PubMed Central - PubMed

Affiliation: MRC Centre for Causal Analyses in Translational Epidemiology, School of Social and Community Medicine, University of Bristol, U.K. c.macdonald-wallis@bristol.ac.uk

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Average trajectories of (a) weight and (b) mean arterial pressure across pregnancy predicted by univariate multilevel linear spline models (N = 11,650).
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fig02: Average trajectories of (a) weight and (b) mean arterial pressure across pregnancy predicted by univariate multilevel linear spline models (N = 11,650).

Mentions: where is the first gestational age spline for weight, up to 18 weeks' gestation, is the second spline, from 18 − 29 weeks, and is the third spline, from 29 weeks onwards. For MAP is the first gestational age spline, up to 18 weeks' gestation, is the second spline, from 18 − 29 weeks, is the third spline, from 29 − 36 weeks and is the fourth spline, from 36 weeks onwards. The shape of the average weight and MAP trajectories across gestation predicted from these models are shown in Figure 2 , and Figure S1 shows example predicted trajectories of weight and MAP for five individual women.


Multivariate multilevel spline models for parallel growth processes: application to weight and mean arterial pressure in pregnancy.

Macdonald-Wallis C, Lawlor DA, Palmer T, Tilling K - Stat Med (2012)

Average trajectories of (a) weight and (b) mean arterial pressure across pregnancy predicted by univariate multilevel linear spline models (N = 11,650).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3569877&req=5

fig02: Average trajectories of (a) weight and (b) mean arterial pressure across pregnancy predicted by univariate multilevel linear spline models (N = 11,650).
Mentions: where is the first gestational age spline for weight, up to 18 weeks' gestation, is the second spline, from 18 − 29 weeks, and is the third spline, from 29 weeks onwards. For MAP is the first gestational age spline, up to 18 weeks' gestation, is the second spline, from 18 − 29 weeks, is the third spline, from 29 − 36 weeks and is the fourth spline, from 36 weeks onwards. The shape of the average weight and MAP trajectories across gestation predicted from these models are shown in Figure 2 , and Figure S1 shows example predicted trajectories of weight and MAP for five individual women.

Bottom Line: Growth models are commonly used in life course epidemiology to describe growth trajectories and their determinants or to relate particular patterns of change to later health outcomes.We discuss linear spline multilevel models with a multivariate response and show how these can be used to relate rates of change in a particular time period in one variable to later rates of change in another variable by using the variances and covariances of individual-level random effects for each of the splines.This method improves on the multivariate linear growth models, which have been used previously to model parallel processes because it enables nonlinear patterns of change to be modelled and the temporal sequence of multivariate changes to be determined, with adjustment for change in earlier time periods.

View Article: PubMed Central - PubMed

Affiliation: MRC Centre for Causal Analyses in Translational Epidemiology, School of Social and Community Medicine, University of Bristol, U.K. c.macdonald-wallis@bristol.ac.uk

Show MeSH
Related in: MedlinePlus