Methods for Retrospective Multi-site Research

回顾性多地点研究方法

• 批准号: 8295621
• 负责人: MARK A ECKERT
• 金额: $ 30.77万
• 依托单位: MEDICAL UNIVERSITY OF SOUTH CAROLINA
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-05-01 至 2017-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8295621
• 关键词: Address; Affect; Archives; Attention deficit hyperactivity disorder; Autistic Disorder; Bayesian Method; Behavior; Behavior Disorders; Behavioral; Biological; Biological Markers; Brain region; Case-Control Studies; Classification; Clinical; Complex; Computer software; Computers; Consensus; Data; Data Analyses; Data Set; Databases; Development; Diagnosis; Disease; Dyslexia; Etiology; Exhibits; Face; Generations; Goals; Heterogeneity; Human; Hybrids; Image; Java; Label; MRI Scans; Measures; Methods; Modeling; Negative Finding; Neurobiology; Phase; Privacy; Procedures; Process; Published Comment; Quality Control; Reading Disabilities; Research; Research Design; Resources; Sample Size; Sampling; Scientist; Site; Software Tools; Source; Speed; Statistical Methods; Time; Treatment Efficacy; Vision; base; comparison group; data sharing; design; effective therapy; efficacy testing; endophenotype; expectation; method development; neurogenetics; neuroimaging; new technology; novel; programs; response; sound; treatment effect; usability

DESCRIPTION (provided by applicant): Methods for retrospective multi-site research will be developed in this project. Integrating data from multiple existing sources has the potential to substantially advance the study of behavior, particularly with respect to understanding complex behavioral disorders. Dyslexia is used as a model to develop methods for complex disorders because of the significant behavioral heterogeneity across people with dyslexia that could be used to develop a richer understanding of reading disability and other complex disorders with equally varied behavioral profiles such as autism or attention deficit hyperactivity disorder. Methods for three critical phases of retrospective multi-site research will be developed. Aim 1 is focused on subject privacy, including the development of an automated data de-identification software tool that can be used for multi-dimensional data sets. Aim 2 is focused on characterizing the behavioral heterogeneity within and across samples of complex disorders, [including the use of multiple imputation to address missing-ness in multi-site data sets.] Aim 3 is focused on how to appropriately analyze data from different samples that have included matched and unmatched case-control study designs. Our goal is to enhance the quality control and scientific power for behavioral and biologic studies of complex disorders, as well as advance the behavioral and neurobiological understanding of dyslexia. By developing standards for integrating behavioral and biological data, retrospective databases could be used to reveal etiologies and establish a consensus for the effective treatment of complex behavioral disorders. PUBLIC HEALTH RELEVANCE: Integrating data from existing sources has the great potential to speed the identification of endophenotypes and their neurogenetic origins for complex behavioral disorders, develop biomarkers for tracking disease and treatment efficacy, and evaluate treatment effects. This project focuses on the development of methods for retrospective multi-site behavioral and biological studies, using dyslexia as a model. The results will establish standards for analysis of multi-site data that will enhance quality control and statistical power.

描述（由申请人提供）：该项目将开发回顾性多站点研究的方法。从多个现有来源整合数据有可能大大推进行为研究，尤其是在理解复杂的行为障碍方面。阅读障碍被用作开发复杂疾病的方法，因为遍及阅读障碍的人具有重要的行为异质性，可用于对阅读障碍和其他具有相同行为特征的复杂性疾病（如自闭症或注意力缺陷多动障碍）的更丰富的了解。将开发回顾性多站点研究的三个关键阶段的方法。 AIM 1专注于主题隐私，包括开发可用于多维数据集的自动数据去识别软件工具。 AIM 2的重点是表征复杂疾病样本内外的行为异质性，[包括使用多个插入来解决多站点数据集中的缺失性。] AIM 3专注于如何适当地分析包括匹配和无与伦比的病例对照研究的不同样本的数据。我们的目标是增强复杂疾病行为和生物学研究的质量控制和科学能力，并提高对阅读障碍的行为和神经生物学理解。通过制定整合行为和生物学数据的标准，回顾性数据库可用于揭示病因并建立有效治疗复杂行为障碍的共识。 公共卫生相关性：集成现有来源的数据具有巨大的潜力，可以加快鉴定内表型及其神经遗传学起源，以使其为复杂的行为障碍，开发用于跟踪疾病和治疗功效的生物标志物，并评估治疗效果。该项目的重点是使用阅读障碍作为模型的回顾性多站点行为和生物学研究的发展。结果将建立分析多站点数据的标准，以增强质量控制和统计能力。