Evaluation of multiple medication exposures concurrently using a novel algorithm

项目总结 大型观测健康数据库(OHD)的发展扩大了可供分析的数据 通过药物流行病学研究。这些研究的效率可以通过同时进行 研究多种药物与感兴趣的疾病之间的关联。不幸的是，先前的研究已经 证明了在研究多个结果时，很难区分真阳性和假阳性结果 同时暴露，从而限制了从这些类型的研究中得出的结论，并代表了 在这一领域有很大差距。这项提案的目标是实现申请人长期-- 改进胃肠道疾病诊断和治疗的学期目标 OHD，是为了评估和验证药物类别浓缩分析(MCEA)，这是一种新的基于SET的信号到 申请人开发的噪声丰富算法，用于分析来自高强度OHD的多次曝光 敏感性和特异性。这一建议的中心假设是，MCEA具有同等的敏感性和 与最广泛使用的OHD分析方法Logistic回归相比，具有更强的特异性 确定药物治疗和临床结果之间的真实关联。申请人须完成以下表格 以下两个相互关联的具体目标来检验这一假设：目标1--计算灵敏度和 药物类别富集度分析(MCEA)和Logistic回归(LR)鉴别的特异性 药物与艰难梭菌感染(CDI)和AIM 2的相关性-计算敏感性和 MCEA和LR用于确定药物与胃肠道出血(GIH)的相关性的特异性。这个 这些目的的基本原理是，通过复制已知的药物-疾病关联而没有假阳性， 未来MCEA可用于确定与胃肠道疾病有关的新的药理作用 学习。拟议研究的预期结果是，它将证明MCEA是一种有效的方法 在药物流行病学研究方面，为研究多暴露OHD开辟了新的研究机会。 这些新的研究机会可能会导致更快地确定潜在的药理学原因 新出现的疾病和发现意想不到的有益药物效果，使此类药物能够 重新调整用途以适应新的适应症。为达到预期效果，申请者将完成额外的 在他的临床流行病学硕士学位的基础上学习计算生物学的课程， 机器学习和计量经济学技术。在这笔赠款和他的机构的支持下，他还将 直接将这些技术应用于药物流行病学的应用 精心挑选的教职员工团队，在胃肠病学、药物流行病学、 医疗信息学，以及指导以前的K奖获得者。通过这些活动，申请者将 培养获得NIH R01级别资金所需的技能，并成为开发小说的领导者 胃肠道疾病流行病学研究中的应用技术。

PROJECT SUMMARY The development of large observational health databases (OHD) has expanded the data available for analysis by pharmacoepidemiology research. The efficiency of these studies may be improved by simultaneously studying the association of multiple medications with a disease of interest. Unfortunately, prior research has demonstrated that it is difficult to distinguish true-positive from false-positive results when studying multiple exposures simultaneously, thus limiting the conclusions drawn from these types of studies and representing a major gap in the field. The objective of this proposal, which is the first step in achieving the applicant's long- term goal of improving the diagnosis and treatment of gastrointestinal diseases using insights derived from OHD, is to evaluate and validate medication class enrichment analysis (MCEA), a novel set-based signal-to- noise enrichment algorithm developed by the applicant to analyze multiple exposures from OHD with high sensitivity and specificity. The central hypothesis of this proposal is that MCEA has equal sensitivity and greater specificity compared to logistic regression, the most widely used analytic method for OHD, for identifying true associations between medications and clinical outcomes. The applicant will complete the following two interrelated specific aims to test the hypothesis: Aim 1 – to calculate the sensitivity and specificity of medication class enrichment analysis (MCEA) and logistic regression (LR) for identifying medication associations with Clostridium difficile infection (CDI) and Aim 2 – to calculate the sensitivity and specificity of MCEA and LR for identifying medication associations with gastrointestinal hemorrhage (GIH). The rationale for these aims is that by reproducing known medication-disease associations without false positives, MCEA can be used to identify novel pharmacologic associations with gastrointestinal diseases in future studies. The expected outcome for the proposed research is that it will demonstrate MCEA as a valid method for pharmacoepidemiology research, opening new research opportunities for the study of multi-exposure OHD. These new research opportunities may lead to more rapid identification of potential pharmacologic causes of emerging diseases and discovery of unanticipated beneficial medication effects, allowing such medications to be repurposed for new indications. To attain the expected outcome, the applicant will complete additional coursework that builds on his Master of Science in Clinical Epidemiology to learn computational biology, machine learning, and econometrics techniques. With the support of this grant and his institution, he will also directly apply these techniques to pharmacoepidemiology applications under the close mentorship of a carefully selected team of faculty with extensive experience in gastroenterology, pharmacoepidemiology, medical informatics, and mentoring prior K-award grant recipients. Through these activities, the applicant will develop the skills necessary to obtain NIH R01-level funding and become a leader in developing novel techniques for application to the epidemiologic study of gastrointestinal diseases.