Methods for Retrospective Multi-site Research

描述（由申请人提供）：本项目将开发回顾性多中心研究的方法。整合来自多个现有来源的数据有可能大大推进行为研究，特别是在理解复杂的行为障碍方面。阅读障碍被用作开发复杂障碍方法的模型，因为阅读障碍患者之间存在显著的行为异质性，可以用于更深入地了解阅读障碍和其他具有同样不同行为特征的复杂障碍，如自闭症或注意力缺陷多动障碍。将制定回顾性多地点研究的三个关键阶段的方法。目标1专注于受试者隐私，包括开发可用于多维数据集的自动数据去识别软件工具。目标2的重点是表征复杂疾病样本内和样本间的行为异质性，[包括使用多重插补来解决多站点数据集的缺失。目标3的重点是如何适当地分析来自不同样本的数据，包括匹配和不匹配的病例对照研究设计。我们的目标是加强复杂疾病的行为和生物学研究的质量控制和科学力量，以及推进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：整合现有来源的数据具有很大的潜力，可以加速识别复杂行为障碍的内在表型及其神经遗传起源，开发用于跟踪疾病和治疗效果的生物标志物，并评估治疗效果。该项目的重点是发展的方法，回顾性多站点的行为和生物学研究，使用阅读障碍作为一个模型。研究结果将为多地点数据的分析建立标准，从而加强质量控制和统计能力。

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.