Improving Methods for Comparative Effectiveness Research in Cardiovascular Care

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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.