Validating GIS-based methods to address spatial uncertainty in clinical trials

验证基于 GIS 的方法来解决临床试验中的空间不确定性

基本信息

批准号：
8707798
负责人：
Jane Ellen Clougherty
金额：
$ 36.43万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-08-01 至 2017-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8707798
关键词：
Accounting Address Adrenal Cortex Hormones Air Pollution Area Asthma Attention Azithromycin Boston Censuses Chicago Child Cities Classification Clinical Clinical Trials Clinical Trials Network Collaborations Communities Crime Data Data Aggregation Data Set Databases Development Diagnosis Environmental Exposure Environmental Risk Factor Epidemiology Equilibrium Face Failure Geographic Information Systems Goals Gold Hand functions Health Investigation Lower respiratory tract structure Methods Modeling Neighborhoods Oral Participant Performance Police Pollution Poverty Protocols documentation Randomized Recruitment Activity Research Resources Role San Francisco Site Source Statistical Methods Surface Surveys Symptoms Techniques Testing Treatment Efficacy Uncertainty United States National Institutes of Health Upper respiratory tract Validation Variant Violence Vitamin D base clinical effect cohort density improved indexing instrument land cover land use multi-site trial novel prevent residence social stressor trafficking treatment effect treatment response urban area

项目摘要

DESCRIPTION (provided by applicant): Spatial uncertainty is shown to bias epidemiological results through: (1) geocoding error, (2) boundary problems (e.g., mis-classification near boundaries, inappropriate boundaries), and (3) spatial autocorrelation in cohorts and exposures. These spatial errors are particularly problematic for clinical trials - especially where cohorts ar clustered (e.g., near recruitment centers), or spatially stratified in multi-center trials - becaus spatially-distributed social and environmental exposures may influence treatment response. We hypothesize that: (1) Spatial error arising from geocoding error, boundary effects, or spatial autocorrelation likely varies by city; (2) Spatially-distributed social and environmental exposures not randomized in clinical trials, may partially explain or modify treatment effects; (3) The influence of social and environmental factors on observed treatment effects may be influenced by spatial error - thus, treatment effect estimates may be refined by accounting for each. In collaboration with AsthmaNet, a national multi-site network of clinical trials for asthma, we will develop and analyze a multi-region database of GIS-based data on social and environmental exposures previously associated with asthma. We will quantify the impact of spatial errors on exposure estimates and clinical trial results - using this unique resource of clinical trials recruted and conducted using exactly the same protocols in nine very different urban areas (Albuquerque, Atlanta, Boston, Chicago, Denver, Milwaukee, Pittsburgh, San Francisco, Winston-Salem). Specifically, we will: (1) examine and ground-truth multiple geocoding techniques, with attention to differential performance by study site; (2) explore impacts of boundary error on social and environmental exposure assignment; (3) explore the effect of accounting for spatial autocorrelation and spatial error on exposure assignment and clinical effect estimates; and (4) derive best practices for spatially-informed examination of clinical tria results. To our knowledge, this will be the first study leveraging GIS-based information to inform upon the validation and interpretation of clinical trials. Spatial techniques developed and validated under this project will improve our understanding of spatial error and its influence on effect estimates for multiple exposures in asthma. It will also improve our ability to account for multiple exposures and spatial error in clinical trials, thus better tailoring clinical interventios.

描述（由申请人提供）：空间不确定性通过以下方式使流行病学结果产生偏差：(1) 地理编码误差，(2) 边界问题（例如，边界附近的错误分类、不适当的边界），以及 (3) 队列中的空间自相关和曝光。这些空间误差对于临床试验来说尤其成问题，尤其是在队列聚集（例如，招募中心附近）或多中心试验中空间分层的情况下，因为空间分布的社会和环境暴露可能会影响治疗反应。我们假设：（1）由地理编码误差、边界效应或空间自相关引起的空间误差可能因城市而异； (2) 空间分布的社会和环境暴露在临床试验中未随机化，可能部分解释或改变治疗效果； (3) 社会和环境因素对观察到的治疗效果的影响可能会受到空间误差的影响——因此，治疗效果估计可以通过考虑每一个因素来细化。我们将与全国多站点哮喘临床试验网络 AsthmaNet 合作，开发和分析基于 GIS 的多区域数据库，该数据库包含先前与哮喘相关的社会和环境暴露数据。我们将量化空间误差对暴露估计和临床试验结果的影响 - 利用这一独特的临床试验资源，在九个截然不同的城市地区（阿尔伯克基、亚特兰大、波士顿、芝加哥、丹佛、密尔沃基、匹兹堡、旧金山、温斯顿-塞勒姆）。具体来说，我们将：（1）检查并验证多种地理编码技术，注意研究地点的差异性能；（2）探讨边界误差对社会和环境暴露分配的影响；（3）探讨考虑空间自相关和空间误差对暴露分配和临床效果估计的影响； (4) 得出临床试验结果的空间信息检查的最佳实践。据我们所知，这将是第一项利用基于 GIS 的信息来为临床试验的验证和解释提供信息的研究。该项目开发和验证的空间技术将提高我们对空间误差及其对哮喘多次暴露效果估计的影响的理解。它还将提高我们在临床试验中考虑多重暴露和空间误差的能力，从而更好地调整临床干预措施。