Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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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 -