Improving Methods for Comparative Effectiveness Research in Cardiovascular Care

描述(由申请人提供)：不断上涨的医疗保健成本促进了对确保资金合理使用的机制的需求，以及对良好的比较有效性(CE)信息的需求。申请者接受过心脏病学和先进流行病学方法方面的培训，具有丰富的CE研究经验，希望利用大型数据库改进心血管疾病CE研究的方法，这些数据库可能会产生高度概括性和直接适用的CE证据。具体地说，申请者将1)开发新的数据库，通过将Medicaid、州药房援助计划和Medicare的大型索赔数据库与CAD和HF的大型临床登记联系起来，开发新的数据库来研究心力衰竭(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)仪器变量分析，以及C)倾向评分校准，以对抗由于索赔数据研究中缺乏详细的临床信息而导致的偏见，评估HF和CAD的治疗CE。在评价这些分析技术在索赔数据分析中的有效性时，将使用将索赔和登记处联系起来的新数据库作为黄金标准。当索赔数据缺乏关于潜在混杂因素(例如，疾病严重程度)的信息时，CAD将作为一个例子，而当索赔数据也缺乏潜在的影响修正因素(例如，射血分数)时，HF将作为一个例子。这些建议的方法将使用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(ARB)的对比；2)阿托伐他汀与其他他汀类药物在急性冠脉综合征后的对比；3)ACEIs与ARBS对HF的对比；以及4)植入式心律转复除颤器与药物治疗的对比。申请者将完全访问上述数据源，并得到以下合作者的全力支持：Sebastian Schneweiss博士(流行病学方法)、Robert Glynn博士(统计学)、Lynne Stevenson博士(心血管护理)、Francis Cook博士(数据挖掘)和Richard Gliklich博士(登记处)。申请者将报名参加课程和参加统计研讨会，以磨练先进的分析方法的技能。她亦会出席有关心血管疾病的本地及国家研讨会/会议，以更新相关的临床知识。这一奖项将对申请者的发展起到重要作用，使其成为一名杰出的研究人员，能够在心血管疾病的CE研究方面提供领导，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.