Optimal design for longitudinal studies to estimate pubertal height growth in individuals.
Optimal design for longitudinal studies to estimate pubertal height growth in individuals.
复制标题
DOI：
10.1080/03014460.2018.1453948
复制
发表时间：
2018-06
影响因子：
1.7
通讯作者：
Cole TJ
中科院分区：
文献类型：
--
作者：
Cole TJ
背景：SITAR模型用平均身高、峰值身高速度（peak height velocity， PHV）和峰值身高速度时的年龄来表达个体青春期身高的增长。目的：利用SITAR技术确定最佳的测量时间间隔，以总结个体青春期身高的增长情况。对象和方法：使用SITAR（平移和旋转叠加）混合效应生长曲线模型分析了基督医院学校3172名9-19岁男孩的身高，测量了128679次（每个男孩42个身高的中位数），该模型估计了平均曲线和三个特定学科的随机效应。分别对测量间隔为2、3、4、6、12和24个月的数据子集进行模型拟合，并对不同模型进行比较。结果：间隔2-12个月的模型对残差标准偏差（0.8 cm）、平均样条曲线（6个自由度）和随机效应（相关性>0.9）给出了有效相同的结果，表明比每年测量身高更频繁没有好处。2年间隔的模型稍微不太适合，但每个人只需要4到5次测量。结论：青春期身高只需要每年测量一次，精度略低，两年测量四次就足够了。
Background: The SITAR model expresses individual pubertal height growth in terms of mean size, peak height velocity (PHV) and age at PHV. Aim: To use SITAR to identify the optimal time interval between measurements to summarise individual pubertal height growth. Subjects and methods: Heights in 3172 boys aged 9–19 years from Christ’s Hospital School measured on 128 679 occasions (a median of 42 heights per boy) were analysed using the SITAR (SuperImposition by Translation And Rotation) mixed effects growth curve model, which estimates a mean curve and three subject-specific random effects. Separate models were fitted to sub-sets of the data with measurement intervals of 2, 3, 4, 6, 12 and 24 months, and the different models were compared. Results: The models for intervals 2–12 months gave effectively identical results for the residual standard deviation (0.8 cm), mean spline curve (6 degrees of freedom) and random effects (correlations >0.9), showing there is no benefit in measuring height more often than annually. The model for 2-year intervals fitted slightly less well, but needed just four-to-five measurements per individual. Conclusions: Height during puberty needs to be measured only annually and, with slightly lower precision, just four biennial measurements can be sufficient.
登录
查看更多内容
影响因子：
7.7
作者：
Cole TJ;Donaldson MD;Ben-Shlomo Y
通讯作者：
Ben-Shlomo Y
影响因子：
3.2
作者：
Cole TJ;Ahmed ML;Preece MA;Hindmarsh P;Dunger DB
通讯作者：
Dunger DB
影响因子：
1.7
作者：
Cole TJ;Pan H;Butler GE
通讯作者：
Butler GE
影响因子：
1.3
作者：
Morara, Michele;Ryan, Louise;Strauss, Warren
通讯作者：
Strauss, Warren
影响因子：
7.7
作者：
Kuh D;Muthuri SG;Moore A;Cole TJ;Adams JE;Cooper C;Hardy R;Ward KA
通讯作者：
Ward KA