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A discussion of statistical methods to characterise early growth and its impact on bone mineral content later in childhood |
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| Authors: | Sarah R Crozier William Johnson Tim J Cole Corrie Macdonald-Wallis Graciela Muniz-Terrera Hazel M Inskip |
| Affiliation: | 1. MRC Lifecourse Epidemiology Unit, Southampton General Hospital, University of Southampton, Southampton, UK;2. School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, Leicestershire, UK;3. Population, Policy and Practice Programme, UCL Great Ormond Street Institute of Child Health, London, UK;4. MRC Integrative Epidemiology Unit, Oakfield House, University of Bristol, Bristol, UK;5. Department of Population Health Sciences, Oakfield House, Bristol, UK;6. Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK |
| 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. |
| Keywords: | Growth mixture models lifecourse epidemiology linear spline models multilevel models SITAR |
