Please use this identifier to cite or link to this item: https://doi.org/10.1177/0962280213503925
Title: Linear spline multilevel models for summarising childhood growth trajectories: A guide to their application using examples from five birth cohorts
Authors: Howe, L.D
Tilling, K
Matijasevich, A
Petherick, E.S
Santos, A.C
Fairley, L
Wright, J
Santos, I.S
Barros, A.J.D
Martin, R.M
Kramer, M.S 
Bogdanovich, N
Matush, L
Barros, H
Lawlor, D.A
Keywords: childhood
clinical article
controlled study
drawing
economic development
height
human
measurement error
statistical model
workflow
body height
body weight
child
child development
cohort analysis
female
infant
male
newborn
nonlinear system
preschool child
Body Height
Body Weight
Child
Child Development
Child, Preschool
Cohort Studies
Female
Humans
Infant
Infant, Newborn
Linear Models
Male
Nonlinear Dynamics
Issue Date: 2016
Publisher: SAGE Publications Ltd
Citation: Howe, L.D, Tilling, K, Matijasevich, A, Petherick, E.S, Santos, A.C, Fairley, L, Wright, J, Santos, I.S, Barros, A.J.D, Martin, R.M, Kramer, M.S, Bogdanovich, N, Matush, L, Barros, H, Lawlor, D.A (2016). Linear spline multilevel models for summarising childhood growth trajectories: A guide to their application using examples from five birth cohorts. Statistical Methods in Medical Research 25 (5) : 1854-1874. ScholarBank@NUS Repository. https://doi.org/10.1177/0962280213503925
Rights: Attribution 4.0 International
Abstract: Childhood growth is of interest in medical research concerned with determinants and consequences of variation from healthy growth and development. Linear spline multilevel modelling is a useful approach for deriving individual summary measures of growth, which overcomes several data issues (co-linearity of repeat measures, the requirement for all individuals to be measured at the same ages and bias due to missing data). Here, we outline the application of this methodology to model individual trajectories of length/height and weight, drawing on examples from five cohorts from different generations and different geographical regions with varying levels of economic development. We describe the unique features of the data within each cohort that have implications for the application of linear spline multilevel models, for example, differences in the density and inter-individual variation in measurement occasions, and multiple sources of measurement with varying measurement error. After providing example Stata syntax and a suggested workflow for the implementation of linear spline multilevel models, we conclude with a discussion of the advantages and disadvantages of the linear spline approach compared with other growth modelling methods such as fractional polynomials, more complex spline functions and other non-linear models. © SAGE Publications.
Source Title: Statistical Methods in Medical Research
URI: https://scholarbank.nus.edu.sg/handle/10635/179286
ISSN: 0962-2802
DOI: 10.1177/0962280213503925
Rights: Attribution 4.0 International
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