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）：测量误差的存在被认为是与自我报告的饮食评估相关的不可避免的条件，并且测量误差在减弱或扭曲生活方式方面的作用在干预中是众所周知的。在试验中，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在整个干预过程中重复测量的结果变量，在这种情况下，测量误差可能会通过减少干预效果来影响干预效果的估计。权力因此，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，以响应 PAR-09-224：改善饮食和健康行为。在体育活动评估中，我们建议开发一个统计框架，以纠正生活方式纵向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包含自我报告和客观测量的验证研究。（即恢复使用这些数据集，我们将估计自我报告的饮食测量和客观的饮食测量之间的关系，并借用这些信息来纠正仅包括自我报告的饮食测量的纵向生活方式干预试验中的测量误差。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，这种方法有很多优点，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；使用敏感性分析来解决无法验证的后续推论的测量误差假设；3）可以估算饮食的校正测量结果，以便测量误差校正数据集的用户可以使用标准统计方法进行分析。修正为利用外部验证数据来消除纵向自我报告饮食数据中的测量误差，我们的具体目标是：1）在进行纵向分析以估计治疗效果时，研究测量误差对自我报告饮食结果的影响；纠正纵向生活方式干预中测量误差的统计框架，其中测量结果存在误差，测量误差可能随时间变化，并且治疗组之间可能有所不同 3) 开发和评估使用倾向评分方法将外部验证研究与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衡量生活方式干预的效果及其机制。采取更有效的干预措施来改善高危人群的饮食。