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

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Multivariate multilevel spline models for parallel growth processes : application to weight and mean arterial pressure in pregnancy. / Macdonald-Wallis, Corrie; Lawlor, Debbie A.; Palmer, Tom; Tilling, Kate.

In: Statistics in Medicine, Vol. 31, No. 26, 20.11.2012, p. 3147-3164.

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Macdonald-Wallis, Corrie ; Lawlor, Debbie A. ; Palmer, Tom ; Tilling, Kate. / Multivariate multilevel spline models for parallel growth processes : application to weight and mean arterial pressure in pregnancy. In: Statistics in Medicine. 2012 ; Vol. 31, No. 26. pp. 3147-3164.

Bibtex

@article{ce163f73a3b14d5abf4513a8aa143b70,
title = "Multivariate multilevel spline models for parallel growth processes: application to weight and mean arterial pressure in pregnancy",
abstract = "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. However, methods to analyse relationships between two or more change processes occurring in parallel, in particular to assess evidence for causal influences of change in one variable on subsequent changes in another, are less developed. 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. We describe how regression coefficients can be calculated for these associations and how these can be adjusted for other parameters such as random effect variables relating to baseline values or rates of change in earlier time periods, and compare different methods for calculating the standard errors of these regression coefficients. We also show that these models can equivalently be fitted in the structural equation modelling framework and apply each method to weight and mean arterial pressure changes during pregnancy, obtaining similar results for multilevel and structural equation models. 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.",
keywords = "Adult, Biometry, Blood Pressure, Databases, Factual, Female, Growth, Humans, Infant, Newborn, Linear Models, Male, Models, Biological, Models, Statistical, Multivariate Analysis, Pregnancy, Weight Gain, Young Adult",
author = "Corrie Macdonald-Wallis and Lawlor, {Debbie A.} and Tom Palmer and Kate Tilling",
note = "Copyright {\textcopyright} 2012 John Wiley & Sons, Ltd.",
year = "2012",
month = nov,
day = "20",
doi = "10.1002/sim.5385",
language = "English",
volume = "31",
pages = "3147--3164",
journal = "Statistics in Medicine",
issn = "0277-6715",
publisher = "John Wiley and Sons Ltd",
number = "26",

}

RIS

TY - JOUR

T1 - Multivariate multilevel spline models for parallel growth processes

T2 - application to weight and mean arterial pressure in pregnancy

AU - Macdonald-Wallis, Corrie

AU - Lawlor, Debbie A.

AU - Palmer, Tom

AU - Tilling, Kate

N1 - Copyright © 2012 John Wiley & Sons, Ltd.

PY - 2012/11/20

Y1 - 2012/11/20

N2 - 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. However, methods to analyse relationships between two or more change processes occurring in parallel, in particular to assess evidence for causal influences of change in one variable on subsequent changes in another, are less developed. 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. We describe how regression coefficients can be calculated for these associations and how these can be adjusted for other parameters such as random effect variables relating to baseline values or rates of change in earlier time periods, and compare different methods for calculating the standard errors of these regression coefficients. We also show that these models can equivalently be fitted in the structural equation modelling framework and apply each method to weight and mean arterial pressure changes during pregnancy, obtaining similar results for multilevel and structural equation models. 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.

AB - 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. However, methods to analyse relationships between two or more change processes occurring in parallel, in particular to assess evidence for causal influences of change in one variable on subsequent changes in another, are less developed. 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. We describe how regression coefficients can be calculated for these associations and how these can be adjusted for other parameters such as random effect variables relating to baseline values or rates of change in earlier time periods, and compare different methods for calculating the standard errors of these regression coefficients. We also show that these models can equivalently be fitted in the structural equation modelling framework and apply each method to weight and mean arterial pressure changes during pregnancy, obtaining similar results for multilevel and structural equation models. 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.

KW - Adult

KW - Biometry

KW - Blood Pressure

KW - Databases, Factual

KW - Female

KW - Growth

KW - Humans

KW - Infant, Newborn

KW - Linear Models

KW - Male

KW - Models, Biological

KW - Models, Statistical

KW - Multivariate Analysis

KW - Pregnancy

KW - Weight Gain

KW - Young Adult

U2 - 10.1002/sim.5385

DO - 10.1002/sim.5385

M3 - Journal article

C2 - 22733701

VL - 31

SP - 3147

EP - 3164

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 0277-6715

IS - 26

ER -