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

描述（由申请人提供）：空间不确定性显示通过以下方式使流行病学结果产生偏差：（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）队列和暴露的空间自相关。这些空间误差对于临床试验来说是特别成问题的-特别是在群组聚类的情况下（例如，招募中心附近），或在多中心试验中进行空间分层-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）在临床试验中未随机化的空间分布的社会和环境暴露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受到空间误差的影响-因此，可以通过考虑每一个来细化处理效果估计。在与AsthmaNet（一个全国性的多站点哮喘临床试验网络）的合作中，我们将开发和分析一个基于GIS的多区域数据库，该数据库包含以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，这些临床试验是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、弗朗西斯科、温斯顿-塞勒姆）招募和实施的，使用完全相同的方案。具体而言，我们将：（1）检查和地面实况多重地理编码技术，注意研究中心的差异性能;（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的信息来验证和解释临床试验的研究。本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应估计的影响的理解。它还将提高我们在临床试验中解释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.