Longitudinal Within-Individual Variability of Standardized-Somatometric-Measures

描述（由申请人提供）：背景：研究设计的功效计算严重依赖于变异性的估计，但对儿童和青少年各种标准化身体测量指标的个体内变异性几乎没有经验性纵向数据。个体内个体差异的来源可能是真正的纵向差异和/或偏倚/测量误差。大的个体内变异性可能会掩盖儿童肥胖研究中治疗或预防干预措施的真实治疗效果，并且无效的干预措施可能会虚假地表现为有效。研究应具有适当的样本量，以说明这种变异性，并应选择对这种变异性不太敏感的身体测量指标作为研究终点。我们的假设是，在一个务实的初级保健环境中的儿童/青少年的显着比例将有很大的个体内变异性。设计图：回顾性队列研究，使用电子健康记录数据（EHR），从相对健康的儿童和青少年在过去14年的初级保健组织。具体目标1a）：探索个体内纵向变异的大小，测量。我们将探索的可变性度量是序列标准化测量与年龄的回归线的斜率（斜率“0”表示与原始增长轨迹的偏差）和重复测量的均方根误差（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、10分位数和10分位数的变异性度量，并探索这些不同的身体测量指标之间的变异性度量的相关性。我们将估计具有较大变异性的儿童的百分比。我们将探讨不同年龄亚组类别的斜率变化。1b）为不同的人体测量指标生成变异性指标的参考标准（根据年龄/性别/种族/民族/社会经济地位）。1c）使用蒙特卡罗模拟，探索有多少纵向个体内变异性可以由某些个体特异性或环境特异性因素解释。我们的队列，与重量为~231000个人和e2的重量在以下三个年龄亚组（2-5/6-12/13-20岁）为~65000个人，是很好的动力来建模和探索这种个体内变异。潜在影响：变异性度量的参考标准的产生将填补设计适当动力研究中的一个重要研究空白。识别对个体内变异性不太敏感的躯体测量指标将导致选择更好的研究终点，这些终点反映了真实的生长轨迹，并在使用常规收集的EHR数据的比较有效性研究和相关流行病学研究中捕获某些干预措施/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.