Improving Methods for Comparative Effectiveness Research in Cardiovascular Care

描述（由申请人提供）：医疗保健成本的上升促使人们需要建立机制，以确保明智地使用资金，并需要良好的相对有效性（CE）信息。申请人接受过心脏病学和先进流行病学方法的培训，在CE研究方面有丰富的经验，希望通过使用大型数据库来改进心血管疾病（CVD）的CE研究方法，这些数据库可能产生高度概括和直接适用的CE证据。具体而言，申请人将1)开发新的数据库，通过将来自医疗补助计划、州药房援助计划和医疗保险的大型索赔数据库与CAD和HF的大型临床登记相联系，研究心力衰竭（HF）和冠心病（CAD）患者的治疗CE； 2)开发和评估3种分析技术的模型：A)使用数据挖掘技术的高维倾向评分；B)工具变量分析；C)倾向评分校准，以消除由于在评估心衰和CAD治疗的CE的索赔数据研究中缺乏详细的临床信息而产生的偏差。在评估索赔数据分析中这些分析技术的有效性时，将使用连接索赔和登记处的新数据库作为金标准。当索赔数据缺乏关于潜在混杂因素的信息（例如，疾病严重程度）时，CAD将作为一个例子，当索赔数据也缺乏潜在的效果调节剂（例如，射血分数）时，HF将作为一个例子。这些建议的方法将通过4个临床相关的CE问题进行评估：1)心肌梗死后血管紧张素转换酶抑制剂（ACEIs）与血管紧张素II受体阻滞剂（ARBs）， 2)急性冠状动脉综合征后阿托伐他汀与其他他汀类药物，3)心力衰竭时ACEIs与ARBs，以及4)心力衰竭时植入式心律转复除颤器与药物治疗。申请人将可以完全访问上述数据源，并得到合作者，博士和博士的全力支持。Sebastian Schneeweiss（流行病学方法），Robert Glynn（统计学），Lynne Stevenson（心血管护理），Francis Cook（数据挖掘）和Richard Gliklich（登记）。申请人将参加统计课程和研讨会，以磨练高级分析方法的技能。她还将参加当地和国家的心血管疾病研讨会/会议，以更新相关的临床知识。该奖项将对申请人的发展发挥重要作用，使其成为一名杰出的研究人员，能够在CVD的CE研究中发挥领导作用，特别是在大型数据库方法方面。

DESCRIPTION (provided by the applicant): Rising cost of healthcare fostered the demand for mechanisms to ensure money being spent wisely and the need for good comparative effectiveness (CE) information. The applicant who was trained in cardiology and advanced epidemiologic methods with a wealth of experience in CE studies aspires to improve methods of CE research for cardiovascular disease (CVD) using large databases that can potentially produce highly generalizable and directly applicable CE evidence. Specifically, the applicant will 1) develop new databases to study CE of therapies in patients with heart failure (HF) and coronary artery disease (CAD) by linking large claims databases from Medicaid, state pharmacy assistance programs, and Medicare with large clinical registries of CAD and HF and 2) develop and evaluate models for 3 analytic techniques A) high dimensional propensity score using data mining techniques, B) instrumental variable analysis, and C) propensity score calibration to combat bias due to lack of detailed clinical information in claims data research assessing CE of therapies in HF and CAD. When evaluating the validity of these analytic techniques in claims data analyses, the new databases linking claims and registries will be used as the gold standard. CAD will serve as an example when claims data lack information on potential confounders (e.g., disease severity) and HF will serve as an example when claims data also lack potential effect modifiers (e.g., ejection fraction). These proposed methods will be assessed using 4 clinically relevant CE questions: 1) angiotensin-converting enzyme inhibitors (ACEIs) vs. angiotensin II receptor blockers (ARBs) after myocardial infarction, 2) atorvastatin vs. other statins after acute coronary syndrome, 3) ACEIs vs. ARBs for HF, and 4) implantable cardioverter-defibrillators vs. medical therapy for HF. The applicant will have a full access to the aforementioned data sources and full support from collaborators, Drs. Sebastian Schneeweiss (epidemiology method), Robert Glynn (statistics), Lynne Stevenson (cardiovascular care), Francis Cook (data mining) and Richard Gliklich (registries). The applicant will enroll in coursework and attend seminars for statistics to hone skills in advanced analytic methods. She will also attend local and national seminars/conferences for CVD to update relevant clinical knowledge. This award would play an important role in the applicant's development as an outstanding researcher who can provide leadership in CE research for CVD, especially in large database methods.