Loughborough University
Leicestershire, UK
LE11 3TU
+44 (0)1509 263171
Loughborough University

Loughborough University Institutional Repository

Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/36608

Title: A discussion of statistical methods to characterize early growth and its impact on bone mineral content later in childhood
Authors: Crozier, Sarah
Johnson, William O.
Cole, Tim J.
Macdonald-Wallis, Corrie
Muniz-Terrera, Graciela
Inskip, Hazel
Tilling, Kate
Keywords: Growth mixture models
Lifecourse epidemiology
Linear spline models
Multilevel models
Issue Date: 2019
Publisher: Taylor & Francis
Citation: CROZIER, S. ... et al., 2019. A discussion of statistical methods to characterize early growth and its impact on bone mineral content later in childhood. Annals of Human Biology, doi:10.1080/03014460.2019.1574896.
Abstract: Background Many statistical methods are available to model longitudinal growth data and relate derived summary measures to later outcomes. Aim To apply and compare commonly used methods to a realistic scenario including pre- and postnatal data, missing data and confounders. Subjects and methods Data were collected from 753 offspring in the Southampton Women’s Survey with measurements of bone mineral content (BMC) at age 6 years. Ultrasound measures included crown-rump length (11 weeks’ gestation) and femur length (19 and 34 weeks’ gestation); postnatally, infant length (birth, 6 and 12 months) and height (2 and 3 years) were measured. A residual growth model, two-stage multilevel linear spline model, joint multilevel linear spline model, SITAR and a growth mixture model were used to relate growth to 6-year BMC. Results Results from the residual growth, two-stage and joint multilevel linear spline models were most comparable: an increase in length at all ages was positively associated with BMC, the strongest association being with later growth. Both SITAR and the growth mixture model demonstrated that length was positively associated with BMC. Conclusions Similarities and differences in results from a variety of analytic strategies need to be understood in the context of each statistical methodology.
Description: This paper is in closed access until 12 months after publication.
Sponsor: Core support for the SWS is provided by the UK Medical Research Council and the Dunhill Medical Trust, with adjunctive support from the European Union's Seventh Framework Programme (FP7/2007-2013), project EarlyNutrition under grant agreement no. 289346. WJ is funded by the Medical Research Council (UK) programme MC_UP_1005/1. TJC is funded by MRC grant: MR/M012069/1. CMW is funded by an MRC research fellowship (MR/J011932/1). The UK Medical Research Council provides funding for the MRC Integrative Epidemiology Unit (MC_UU_12013/9). GMT was funded by NIH/NIA Program Project Grant P01AG043362; 2013-2018. MRC Grant G1000726 supported the collaboration leading to this work.
Version: Accepted for publication
DOI: 10.1080/03014460.2019.1574896
URI: https://dspace.lboro.ac.uk/2134/36608
Publisher Link: https://doi.org/10.1080/03014460.2019.1574896
ISSN: 0301-4460
Appears in Collections:Closed Access (Sport, Exercise and Health Sciences)

Files associated with this item:

File Description SizeFormat
Johnson_ACCEPTED.pdfAccepted version738.08 kBAdobe PDFView/Open


SFX Query

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.