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

说明(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)，以及(3)队列和暴露中的空间自相关性，显示出对流行病学结果的偏差。这些空间误差对于临床试验尤其成问题，特别是在队列聚集(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)地理编码误差、边界效应或空间自相关所产生的空间误差可能因城市而异；(2)临床试验中未随机分布的社会和环境暴露可能部分解释或修改治疗效果；(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，通过考虑每个因素，治疗效果估计可能会被改进。我们将与全国多地点哮喘临床试验网络AsthmaNet合作，开发和分析一个基于地理信息系统的多地区数据库，其中包括以前与哮喘有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-使用这一独特的临床试验资源，招募并在九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)使用完全相同的方案进行临床试验。具体地说，我们将：(1)检查和实地真实的多重地理编码技术，注意研究地点的不同表现；(2)探索边界误差对社会和环境暴露分配的影响；(3)探索考虑空间自相关性和空间误差对暴露分配和临床效果估计的影响；以及(4)为临床试验结果的空间知情检查得出最佳实践。据我们所知，这将是利用基于地理信息系统的信息来通报临床试验的验证和解释的第一项研究。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次暴露和空间误差的能力，从而更好地定制临床干预措施。

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.