A discussion of statistical methods to characterize early growth and its impact on bone mineral content later in childhood
journal contributionposted on 17.01.2019 by Sarah Crozier, Will Johnson, Tim J. Cole, Corrie Macdonald-Wallis, Graciela Muniz-Terrera, Hazel Inskip, Kate Tilling
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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.
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.
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