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

• 批准号: 8518453
• 负责人: Jane Ellen Clougherty
• 金额: $ 35.41万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 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/8518453
• 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

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：(1)地理编码错误，(2)边界问题（例如，边界附近的错误分类，不适当的边界）和(3)队列和暴露的空间自相关性显示出对流行病学结果的偏倚。这些空间误差在临床试验中尤其成问题，特别是在队列聚集的情况下（例如，在招募中心附近），或在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不是随机的，可能部分解释或改变治疗效果；(3)社会和环境因素对观测到的治理效果的影响可能会受到空间误差的影响，因此，可以通过考虑每个因素来改进治理效果的估计。通过与哮喘临床试验的国家多站点网络AsthmaNet合作，我们将开发和分析一个基于gis的与哮喘相关的社会和环境暴露的多区域数据库。我们将量化空间误差对暴露估计和临床试验结果的影响——使用这一独特的临床试验资源，在9个非常不同的城市地区（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）采用完全相同的方案招募和实施。具体而言，我们将：(1)研究和验证多种地理编码技术，并关注不同研究地点的差异表现；(2)探讨边界误差对社会环境暴露分配的影响；(3)探讨空间自相关和空间误差对暴露分配和临床效应估计的影响；(4)得出对临床试验结果进行空间知情检查的最佳实践。据我们所知，这将是第一个利用基于gis的信息来验证和解释临床试验的研究。在该项目下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重暴露影响估计的理解。它还将提高我们在临床试验中解释多重暴露和空间误差的能力，从而更好地定制临床干预措施。