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|Title: ||Linear spline multilevel models for summarising childhood growth trajectories: a guide to their application using examples from five birth cohorts|
|Authors: ||Howe, Laura D.|
Petherick, Emily S.
Santos, Ana Cristina
Santos, Ina S.
Barros, Aluisio J.D.
Martin, Richard M.
Kramer, Michael S.
Lawlor, Debbie A.
|Issue Date: ||2016|
|Publisher: ||© the authors|
|Citation: ||HOWE, L.D. ... et al., 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), pp. 1854–1874.|
|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.|
|Sponsor: ||LDH’s contribution was funded by a Population Health Scientist Fellowship from the UK Medical Research Council (MRC; G1002375). ESP, LF and JW’s contribution was funded by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (Grant Reference Number RP-PG-0407-10044). RMM, MK, NB and LM’s contribution was supported by a grant from the Canadian Institutes of Health Research and by the European Union’s project on Early Nutrition Programming: Longterm Efficacy and Safety Trials (Grant Number FOOD-DT-2005-007036 to RMM). KT’s contribution was funded by a grant from the UK MRC (G1000726). MSK is Senior Investigator of the Canadian Institutes of Health Research. DAL and LDH work in a centre that receives funding from the UK MRC (G0600705) and the University of Bristol. The NIHR Bristol Nutrition Biomedical Research Unit is funded by the NIHR and is
a partnership between University Hospitals Bristol NHS Foundation Trust and the University of Bristol. ACS was funded by the Portuguese Foundation for Science and Technology project (fct-ptdc/sau-ESA/105033/2008) and through a postdoctoral fellowship grant (SFRH/BPD/63112/2009). HB and ACS received funding from Fundacao Calouste Gulbenkian. The UK MRC, the Wellcome Trust and the University of Bristol provide core funding support for ALSPAC. The 2004 Pelotas birth cohort study is currently supported by the Wellcome Trust, UK, entitled Major Awards for Latin America on Health Consequences of Population Change (Grant
No. 086974/Z/08/Z). Previous phases of the study were supported by the WHO, National Support Program for
Centers of Excellence (PRONEX), the Brazilian National Research Council (CNPq), the Brazilian Ministry of
Health and the Children’s Mission.|
|Publisher Link: ||http://dx.doi.org/10.1177/0962280213503925|
|Appears in Collections:||Published Articles (Sport, Exercise and Health Sciences)|
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