Longitudinal Within-Individual Variability of Standardized-Somatometric-Measures

描述(由申请人提供)：背景：研究设计的能量计算严重依赖于变异性的估计，但关于儿童和青少年各种标准化体测测量的个体内变异性的经验性纵向DTA很少。个体内部纵向差异的来源可能是真正的纵向差异和/或偏差/测量误差。个体内部的巨大变异性可能会掩盖儿童肥胖症研究的治疗或预防干预措施的真实治疗效果，相反，无效的干预措施可能会伪装成有效的。研究应该有适当的样本量来解释这种变异性，对这种变异性不那么敏感的体尺测量应该被选为研究终点。我们的假设是，在务实的初级保健环境中，相当大比例的儿童/青少年将具有较大的纵向个体差异。设计：使用电子健康记录数据(EHR)进行的回溯性队列研究，这些数据来自一个初级保健组织在过去14年中相对健康的儿童和青少年。具体目标1a)：探索标准化体测测量的个体内纵向变异性的大小。我们将探讨的变异性指标是系列标准化措施对年龄的回归线的斜率(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年龄变异性指标的z分数、百分位数和百分位数，并探索这些变异性指标在这些不同的人体测量指标之间的相关性。我们将估计具有较大变异性的儿童的百分比。我们将探索标准化措施在不同年龄亚组类别中的斜率变化。1b)为不同的标准化体量测量(根据年龄/性别/种族/族裔/社会经济地位)制定变异性度量的参考标准。1c)使用蒙特卡罗模拟，探索某些特定于个人或特定于环境的因素在多大程度上可以解释个体内的纵向差异。我们的队列有大约231000个个体的权重，以下三个年龄段(2-5/6-12/13-20岁)中每个年龄组的e2权重为约65000个个体，他们很有能力建模和探索这种个体内的变异性。潜在影响：变异性指标参考标准的产生将填补设计适当支持的研究的一个重要研究空白。确定对个体内变异性不太敏感的体测测量将导致选择更好的研究终点，反映真实的生长轨迹，并捕捉使用常规收集的EHR数据进行的比较有效性研究和关联流行病学研究中某些干预/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.