Longitudinal Within-Individual Variability of Standardized-Somatometric-Measures

描述（由申请人提供）：背景：研究设计的功率计算严重依赖于变异性的估计，但是关于儿童和青少年各种标准化身体测量的个体内变异性的经验纵向数据很少。个体内部纵向变异的来源可能是真正的纵向差异和/或偏差/测量误差。在儿童肥胖研究中，个体内部的巨大差异可能会模糊治疗或预防干预措施的真正治疗效果，相反，无效的干预措施可能会看似有效。研究应该有适当的样本量来解释这种可变性，并且应该选择对这种可变性不太敏感的躯体测量方法作为研究终点。我们的假设是，在实用的初级保健环境中，很大比例的儿童/青少年将具有很大的纵向个体内变异性。设计：回顾性队列研究，使用电子健康记录数据（EHR），来自一家初级保健机构过去14年来相对健康的儿童和青少年。具体目标1a)：探索标准化人体测量的纵向个体变异的大小。我们将探讨的变异性指标是一系列标准化措施的回归线对年龄的斜率（斜率' 0表示偏离原始增长轨迹）和标准化措施的均方根误差（RMSE）（使用分段线性回归模型）。我们将研究体重和bmi年龄z分数的变异性指标，百分位数和百分位数，并探索这些不同的身体测量指标之间的变异性指标的相关性。我们将估计变异较大的儿童的百分比。我们将探讨标准化测量在不同年龄子组类别中的斜率变化。1b)根据年龄/性别/种族/民族/社会经济地位，为不同的标准化身体测量指标生成可变性指标的参考标准。1c)利用蒙特卡罗模拟，探索个体内部的纵向变异性有多少可以用某些特定的个体或特定的环境因素来解释。我们的队列中约有231000个个体的权重，在以下三个年龄亚组（2-5/6-12/13-20岁）中各有e2个权重，约有65000个个体，可以很好地建模和探索这种个体内变异性。潜在影响：可变性指标参考标准的产生将填补设计适当动力研究的重要研究空白。识别对个体内变异性不太敏感的躯体测量方法，将有助于选择更好的研究终点，反映真实的生长轨迹，并在比较有效性研究和使用常规收集的电子健康档案数据的关联流行病学研究中捕捉某些干预措施/因素的影响。

DESCRIPTION (provided by applicant): Background: Power calculations for research designs are critically dependent on estimates of variability, but there are little empirical longitudinal dta on the within-individual variability of various standardized somatometric measures for children and adolescents. Sources of the longitudinal-within- individual variability could be genuine longitudinal differences and/or bias/measurement errors. Large within-individual variability can obscure true treatment effects of therapeutic or preventive interventions for childhood obesity research and reversely, ineffective interventions could spuriously appear to be effective. Studies should have appropriate sample sizes to account for this variability and somatometric measures that are less sensitive to this variability should be selected as study endpoints. Our hypothesis i that a significant percentage of children/adolescents in a pragmatic primary care setting will have large longitudinal-within-individual variability. Design: Retrospective cohort study using electronic health record data (EHR) from relatively healthy children and adolescents in a primary-care- organization over the past 14 years. Specific aim 1a): Explore the size of the longitudinal within- individual variability of standardized-somatometric-measures. The variability-metrics we will probe are the slopes of the regression lines of serial-standardized-measures vs. age (slope`0 indicates deviation from original growth-trajectory) and the root-mean-square-error (RMSE) of the standardized-measures (using piecewise-linear-regression models). We will study variability-metrics for weight and BMI-for-age z-scores, percentiles and centiles and explore the correlation of those variability-metrics between those different somatometric-measures. We will estimate the percentage of children with large variability. We will explore the changes in slope of standardized-measures across different age-subgroup-categories. 1b) Generate reference standards of variability-metrics for different standardized-somatometric- measures (according to age/gender /race/ethnicity/socioeconomic status). 1c) Explore how much of the longitudinal within-individual variability could be explained by certain individual-specific or setting- specific factors, using Monte Carlo simulations. Our cohort, with weights for ~231000 individuals and with e2 weights in each of the following three age-subgroups (2-5/6-12/13-20 years) for ~65000 individuals, is well powered to model and explore this within-individual-variability. Potential impact: The generation of reference-standards for variability-metrics would fill an important research gap in the design appropriately powered studies. Identification of somatometric-measures that are less sensitive to within-individual-variability would lead to selection of better study endpoints that reflect the true growth-trajectories and capture the impact of certain interventions/factors in comparative effectiveness research and association-epidemiologic studies using routinely collected EHR data.