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进了对确保资金明智使用的机制的需求，以及对良好的比较有效性（CE）信息的需求。申请人接受过心脏病学和先进流行病学方法的培训，在CE研究方面拥有丰富的经验，希望使用大型数据库改进心血管疾病（CVD）的CE研究方法，这些数据库可能会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Medicaid、州药房援助计划和Medicare的大型索赔数据库与CAD和HF的大型临床登记研究相关联，研究心力衰竭（HF）和冠状动脉疾病（CAD）患者的治疗CE; 2）开发和评价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，和C）倾向评分校准，以消除由于在评估HF和CAD治疗CE的索赔数据研究中缺乏详细临床信息而导致的偏倚。在评估这些分析技术在索赔数据分析中的有效性时，将把连接索赔和登记的新数据库用作黄金标准。当索赔数据缺乏潜在混杂因素的信息（例如，疾病严重程度）和HF将作为索赔数据也缺乏潜在效应修饰符（例如，射血分数）。将使用4个临床相关CE问题评估这些拟定方法：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（ARB），2）急性冠脉综合征后阿托伐他汀与其他他汀类药物，3）ACEIs与ARB治疗HF，4）植入式心律转复除颤器与药物治疗HF。申请人将完全访问上述数据源，并获得合作者塞巴斯蒂安施内维斯博士（流行病学方法）、罗伯特格林博士（统计学）、林恩史蒂文森博士（心血管护理）、弗朗西斯库克博士（数据挖掘）和理查德格利克利希博士（登记研究）的全力支持。申请人将报名参加课程，并参加统计研讨会，以磨练先进的分析方法的技能。她还将参加当地和国家CVD研讨会/会议，以更新相关临床知识。该奖项将在申请人的发展中发挥重要作用，作为一个杰出的研究人员谁可以提供领导CE研究CVD，特别是在大型数据库的方法。

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.