Crozier Sarah R, Johnson William, Cole Tim J, Macdonald-Wallis Corrie, Muniz-Terrera Graciela, Inskip Hazel M, Tilling Kate
a MRC Lifecourse Epidemiology Unit, Southampton General Hospital , University of Southampton , Southampton , UK.
b School of Sport, Exercise and Health Sciences , Loughborough University , Loughborough, Leicestershire , UK.
Ann Hum Biol. 2019 Feb;46(1):17-26. doi: 10.1080/03014460.2019.1574896. Epub 2019 Apr 15.
Many statistical methods are available to model longitudinal growth data and relate derived summary measures to later outcomes.
To apply and compare commonly used methods to a realistic scenario including pre- and postnatal data, missing data, and confounders.
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 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.
Similarities and differences in results from a variety of analytic strategies need to be understood in the context of each statistical methodology.
有许多统计方法可用于对纵向生长数据进行建模，并将推导得出的汇总指标与后期结果相关联。
将常用方法应用于包括产前和产后数据、缺失数据及混杂因素在内的实际场景，并进行比较。
从南安普敦妇女调查中收集了753名后代的数据，测量了其6岁时的骨矿物质含量（BMC）。超声测量指标包括头臀长（妊娠11周）和股骨长度（妊娠19周和34周）；产后测量了婴儿身长（出生时、6个月和12个月）和身高（2岁和3岁）。使用残差生长模型、两阶段多级线性样条模型、联合多级线性样条模型、SITAR和生长混合模型将生长情况与6岁时的BMC相关联。
残差生长模型、两阶段和联合多级线性样条模型的结果最具可比性：各年龄段身长增加均与BMC呈正相关，最强的关联是与后期生长的关联。SITAR和生长混合模型均表明身长与BMC呈正相关。
需要在每种统计方法的背景下理解各种分析策略结果的异同。
