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Statistical methods to correct for measurement error in self-reported dietary data from lifestyle intervention trials

纠正生活方式干预试验自我报告饮食数据中测量误差的统计方法

基本信息

批准号：
9292372
负责人：
Juned Siddique
金额：
$ 38.93万
依托单位：
NORTHWESTERN UNIVERSITY AT CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-06-01 至 2020-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9292372
关键词：
Address Affect Attenuated Behavior Biological Markers Biomarker of Dietary Intake Computing Methodologies Data Data Set Development Diet Dietary Component Disabled Persons Eating Behavior Ensure Food Goals Health Hour Intervention Intervention Studies Intervention Trial Measurement Measures Methodology Methods Nature Outcome Outcome Measure Participant Patient Self-Report Performance Physical activity Populations at Risk Potassium Process Proteins Protocols documentation Public Health Records Recovery Research Personnel Role Sampling Scoring Method Sodium Statistical Methods Testing Time Treatment Effectiveness Urine Validation Weight Work base cost disorder risk doubly-labeled water effective intervention improved instrument intervention effect lifestyle data lifestyle intervention longitudinal analysis novel public health relevance response simulation specific biomarkers therapy design total energy expenditure treatment effect treatment group urinary validation studies

项目摘要

DESCRIPTION (provided by applicant): The presence of measurement error is considered to be an inevitable condition associated with self-reported diet assessment and the role of measurement error in attenuating or distorting the association between diet and disease risk is well understood. In lifestyle intervention trials, where the goal is to change a participant's weigt or modify their eating behavior, self-reported diet is typically an outcome variable that is measured repeatedly throughout the intervention. In this setting, measurement error can affect the estimation of intervention effects by reducing the power to detect a treatment effect as well as by biasing estimates of treatment effectiveness. As a result, measurement error in intervention studies has handicapped the development of effective interventions aimed at changing and maintaining healthy behaviors. In response to PAR-09-224: Improving Diet and Physical Activity Assessment, we propose to develop a statistical framework to correct for measurement error in self-reported dietary data from longitudinal lifestyle intervention trials where objective validation data do not exist. We have obtained four validation studies that contain both self-reported and objective measures (i.e. recovery biomarkers) of dietary intake. Using these data sets, we will estimate the relationship between self-reported and objective measures of diet and borrow this information in order to correct for measurement error in longitudinal lifestyle intervention trials that only include self-reported dietary measures. Our approach uses a missing data framework that views unmeasured objective data as missing data. There are a number of advantages to this approach including: 1) It allows us draw upon the many computational and statistical methods for handling missing data; 2) It facilitates the use of sensitivity analysis to address the effect of unverifiable measurement error assumptions on subsequent inferences; 3) Corrected measurements of diet can be imputed so that users of measurement error-corrected data sets can use standard statistical methods to perform their analyses. The overall goal of this project is to develop a statistical framework for correcting for measurement error in longitudinal self-reported dietary data which makes use of external validation data. Our specific aims are: 1) Investigate the implications of measurement error in self- reported dietary outcomes when performing longitudinal analyses to estimate treatment effects; 2) Develop and assess a statistical framework to correct for measurement error in longitudinal lifestyle interventions where outcomes are measured with error and measurement error may vary over time and can differ between treatment groups; 3) Develop and assess methods for combining external validation studies with intervention trials using propensity score methods. This work will allow researchers to more accurately and precisely measure the effects of a lifestyle intervention and its mechanisms. This information will facilitate the development of more effective interventions to improve the diet of at-risk populations.

描述（由申请人提供）：认为存在测量误差是与自我报告的饮食评估相关的不可避免的情况，并且很好地理解了测量误差在减弱或扭曲饮食与疾病风险之间的关联方面的作用。在生活方式干预试验中，目标是改变参与者的体重或改变他们的饮食行为，自我报告的饮食通常是一个结果变量，在整个干预过程中反复测量。在这种情况下，测量误差会通过降低检测治疗效果的功效以及使治疗有效性的估计产生偏差来影响干预效果的估计。因此，干预研究中的测量误差阻碍了旨在改变和维持健康行为的有效干预措施的发展。针对PAR-09-224：改善饮食和身体活动评估，我们建议制定一个统计框架，以纠正纵向生活方式干预试验中自我报告的饮食数据中的测量误差，其中不存在客观验证数据。我们已经获得了四项验证研究，其中包含自我报告和饮食摄入量的客观指标（即恢复生物标志物）。使用这些数据集，我们将估计自我报告和客观饮食测量之间的关系，并借用这些信息，以纠正纵向生活方式干预试验中的测量误差，这些试验只包括自我报告的饮食测量。我们的方法使用缺失数据框架，将未测量的客观数据视为缺失数据。这种方法有许多优点，包括：1）它允许我们利用许多计算和统计方法来处理缺失数据; 2）它便于使用敏感性分析来解决无法验证的测量误差假设对后续推断的影响; 3）饮食的校正测量可以被估算，使得测量误差校正数据集的用户可以使用标准统计方法来执行他们的分析。该项目的总体目标是制定一个统计框架，测量误差的纵向自我报告的饮食数据，利用外部验证数据。我们的具体目标是：2）开发和评估统计框架，以校正纵向生活方式干预中的测量误差，其中结果是用误差测量的，并且测量误差可能随时间而变化，并且在治疗组之间可能不同; 3）开发和评估将外部验证研究与使用倾向评分方法的干预试验相结合的方法。这项工作将使研究人员能够更准确和精确地测量生活方式干预的效果及其机制。这一信息将有助于制定采取更有效的干预措施来改善高危人群的饮食。